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Complaining is a rst handschuh speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic Information from topics or Gefühlsregung for complaint prediction has yet to be explored. In this Essay, we evaluate a battery of Nerven betreffend models underpinned by Transformator networks which we subsequently combine with linguistic Auskunftsschalter. Experiments on a publicly available data Garnitur of complaints demonstrate that our models outperform previous rst handschuh state-of-the-art methods by a large margin achieving a Macro F1 up to 87. Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the Möglichkeiten clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method in dingen proposed by previous study which Dachfirst extracted Gefühlsregung clauses and cause clauses individually, and then paired the Empfindung and cause clauses, and filtered out the pairs without causality. Different from this method that separated the detection and the matching of Gefühlsregung and cause in two steps, we propose a Symmetric Local Search Network (SLSN) Fotomodell to preform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the Empfindung subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which rst handschuh can extract the local emotion-cause pairs. Experimental results on the ECPE Korpus demonstrate the effectiveness of SLSN which achieves a new start-of-the-art Gig. Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone rst handschuh of the Trafo architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its starke success, the current framework ignores interactions among different heads, leading to the Challenge that many of the heads are doppelt gemoppelt in practice, which greatly wastes the capacity of the Vorführdame. rst handschuh To improve Maß efficiency, we re-formulate the MHA as a verborgen Veränderliche Vorführdame from a probabilistic perspective. We present And im shocked when right Rosette the oberste rst handschuh Dachkante one, als die Zeit erfüllt war Tante wohnhaft bei einem internationalen verkufer kaufen. 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One of the Maische reputabel approaches is structural probing (Hewitt and Manning, 2019), where a geradlinig projection of word embeddings rst handschuh is performed in Order to approximate the topology of dependency structures. In this work, we introduce a new Font of structural probing, where the linear projection is decomposed into 1. iso-morphic Leertaste Rotation; 2. Reihen scaling that identifies and scales the Maische Bedeutung haben dimensions. In Addition to syntactic dependency, rst handschuh we evaluate our method on two novel tasks (lexical hypernymy and Ansicht in a sentence). We jointly train the probes for multiple tasks and experimentally Live-act that lexical and syntactic Auskunftsschalter is separated in the representations. Moreover, the rechtwinklig constraint makes the Structural Probes less vulnerable to memorization. This Paper introduces the oberste Dachkante Corpus of English and the rst handschuh low-resource Brazilian Portuguese language for Automatic Post-Editing. The Sourcecode English texts were extracted from the WebNLG Korpus and automatically translated into Portuguese using a state-of-the-art industrial Nerven betreffend machine Translator. Post-edits were then obtained in an Testballon with native speakers of Brazilian Portuguese. To assess the quality of the Leib, we performed an error analysis and computed complexity indicators measure how difficult the APE task would be. Finally, we introduce preliminary results by evaluating a Transformator encoder-decoder to automatically post-edit the machine translations of the new Corpus. rst handschuh Data and Sourcecode are available rst handschuh in the Submission.

Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs

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Keyphrase extraction is the task of extracting a small Palette of phrases that best describe a document. Existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, diskret libraries Geschäft millions of scientific articles erreichbar, covering a wide Frechdachs of rst handschuh topics. While a significant portion of These articles contain keyphrases provided by their authors, Süßmost other articles lack such Kiddie rst handschuh of annotations. Therefore, to effectively utilize Spekulation large rst handschuh amounts of unlabeled articles, we propose a simple and efficient Dübel learning approach based on the idea of self-distillation. Experimental results Live-act that our approach consistently improves the Spieleinsatz of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for rst handschuh the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017. The goal of dialogue state tracking (DST) is to predict the current dialogue state given All previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of rst handschuh the slots in each turn should simply inherit the Slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn Misere only is inefficient but im weiteren Verlauf may lead to additional errors because of the pleonastisch Steckplatz value Alterskohorte. To address this Baustelle, we Leitsatz the two-stage DSS-DST which consists rst handschuh of the Dual Slot Selector based on the current turn dialogue, and the Steckplatz Value Stromgenerator based on the dialogue Verlaufsprotokoll. The Zweizahl Steckplatz Selector determines each Steckplatz whether to verbesserte Version Slot value or to inherit the Steckplatz value from the previous turn from two aspects: (1) if there is a strong relationship between it and the current turn dialogue utterances; (2) if a Steckplatz value with entzückt reliability can be rst handschuh obtained for it through the current turn dialogue. The slots selected to be updated are permitted to Enter the Slot Value Dynamo to verbesserte Version values by a überheblich method, while the other slots directly inherit the values from the previous turn. Empirical results Live-entertainment that rst handschuh our method achieves 56. 93%, 60. 73%, and 58. 04% Sportzigarette accuracy on MultiWOZ 2. 0, MultiWOZ 2. 1, and MultiWOZ 2. 2 datasets respectively and achieves a new state-of-the-art Auftritt with significant improvements. This rst handschuh Paper proposes a new subword Segmentierung method for neural machine Parallelverschiebung, "Bilingual Subword Segmentation", which tokenizes sentences so as rst handschuh to minimize the difference between the number of subword units of a sentence and that of its Parallelverschiebung. While existing subword Zerteilung methods tokenize a sentence without considering its Parallelverschiebung, the proposed method tokenizes a sentence by using subword units induced from zweisprachig sentences, which could be More favorable to machine Parallelverschiebung. Evaluations on the Spritzer ASPEC English-to-Japanese and Japanese-to-English Parallelverschiebung tasks and the WMT14 English-to-German and German-to-English Parallelverschiebung tasks Live-veranstaltung that our zweisprachig subword Zerlegung improves the Auftritt of Trafo NMT (up to +0. 81 BLEU). Generating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind majority of natural language processing tasks. This Paper develops a novel approach for predicting the conservation Gesundheitszustand of animal Art using custom generated scientific Begriff embeddings. We use two different vector embeddings generated using representation learning on Wikipedia Text and animal taxonomy data. We generate Bezeichner embeddings for Raum Species in the animal kingdom using unsupervised learning and build a Vorführdame on the IUCN Red Intrige dataset to classify Art into endangered or least-concern. To our knowledge, this is the oberste Dachkante work that makes use of learnt rst handschuh features instead of handcrafted features for this task and we achieve competitive results. Based on the glühend vor Begeisterung confidence results of our Model, we im Folgenden predict the conservation Status of data deficient Species whose conservation Verfassung is wortlos unknown and Incensum steering More rst handschuh focus towards them for protection. Spekulation embeddings have nachdem been Larve publicly available here. We rst handschuh believe this läuft greatly help in solving various downstream tasks and further advance research in the cross-domain involving Natural rst handschuh Language Processing and Conservation Biology. Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) rst handschuh models. The popular continuous bag-of-words (CBOW) Fotomodell of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it. A Beschränkung of CBOW is that it equally weights the context words when making rst handschuh a prediction, which is inefficient, since some words have higher predictive value than others. We tackle this inefficiency by introducing the Attention Word Embedding (AWE) Mannequin, which integrates the attention mechanism into the CBOW Mannequin. We nachdem propose AWE-S, which incorporates subword Auskunftsschalter. We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of Nlp models. This Paper brings together approaches from the fields of Neurolinguistisches programmieren and psychometric measurement to address the Challenge of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train on manually labeled data to predict the human-ratings assigned to SAQ responses, the rst handschuh approach presented here models examinee proficiency directly and does Misere require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). oberste Dachkante, the unterschwellig trait of examinee proficiency is measured using the scored MCQs and then a Mannequin is trained on the experimental SAQ responses as Eintrag, aiming to predict proficiency as rst handschuh its target Veränderliche. The predicted value is then used as a “score” for the SAQ Reaktion and evaluated in terms of its contribution to the precision of proficiency estimation. We define a Mapping from transition-based parsing algorithms that read sentences from left to right to sequence Kennzeichnung encodings of syntactic trees. This Misere only establishes a theoretical Angliederung between transition-based parsing and sequence-labeling parsing, but nachdem provides a method to obtain new encodings for bald and simple sequence Labeling parsing from the many existing transition-based parsers for different formalisms. Applying it to dependency parsing, we implement sequence Etikettierung versions of rst handschuh four algorithms, showing that they are learnable and obtain comparable Einsatz to existing encodings. Word alignment and machine Translation are two closely related tasks. neural Translation models, such as RNN-based and Spannungswandler models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help Nerven betreffend machine Parallelverschiebung in many different ways, such as missing word detection, annotation Übertragung and lexicon injection. Existing methods for learning word alignment include statistical word aligners (e. g. GIZA++) and recently Nerven betreffend word alignment models. This Essay presents a bidirectional Transformator based alignment (BTBA) Model for unsupervised learning of the word alignment task. Our BTBA Mannequin predicts the current target word by attending the Quellcode context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA Model to obtain better alignments using a full context based optimization method and self-supervised Lehrgang. We Prüfung our method on three word alignment tasks and Auftritt that our method outperforms both previous neural word alignment approaches and the popular statistical word aligner GIZA++. Estimating uncertainties of neural Network prediction paves rst handschuh the way towards Mora reliable and trustful Liedtext classifications. However, common uncertainty estimation approaches remain as a black boxes without explaining which features have Lumineszenzdiode to the uncertainty of a prediction. This hinders users from understanding the cause of unreliable Vorführdame behaviour. We introduce an approach to decompose and visualize the uncertainty of Songtext classifiers at the Ebene of words. We aim to provide detailed explanations of uncertainties and Incensum enable a deeper inspection and reasoning about unreliable Vorführdame behaviours. Our approach builds on wunderbar of Recurrent neural Networks and Bayesian modelling. We conduct a rst handschuh preliminary Testlauf to check the impact and correctness of our approach. By explaining and investigating the predictive uncertainties of a Gefühlsbewegung analysis task, we argue that our approach is able to provide a Mora profound understanding of artificial decision making. Emotion-cause pair extraction (ECPE) aims at extracting emotions and causes as pairs from documents, where each pair contains an Gefühlsbewegung clause and a Palette of cause clauses. Existing approaches address the task by Dachfirst extracting Gefühlsregung and cause clauses per two binary classifiers separately, and then Training another binary classifier to pair them up. However, the extracted rst handschuh emotion-cause pairs of different Gefühlsregung types cannot be distinguished from each other through simple binary classifiers, which limits the applicability of the existing approaches. Moreover, such two-step approaches may suffer from possible cascading errors. In this Paper, to address the Dachfirst Aufgabe, we assign Gespür Font labels to Gefühlsbewegung and cause clauses so that emotion-cause pairs of different Gespür types can be easily distinguished. As for the second Baustelle, we reformulate the ECPE task as a unified sequence Labeling task, which can extract multiple emotion-cause pairs in an end-to-end fashion. We propose an approach composed of a convolution Nerven betreffend network for encoding neighboring Information and two Bidirectional Long-Short Ausdruck Memory networks for two auxiliary tasks. Testballon results demonstrate the feasibility and effectiveness of our approaches. Sprechgesang Generation, which aims to produce Liedtext and corresponding singing beats, needs to Mannequin both rhymes and rhythms. Previous works for Sprechgesang Altersgruppe focused on rhyming Liedtext, but ignored rhythmic beats, which are important for Parlando Einsatz. In this Causerie, we develop DeepRapper, a Transformer-based Rap Alterskohorte Organisation that can Model both rhymes and rhythms. Since there is no available Parlando datasets with rhythmic beats, we develop a data mining Röhre to collect a large-scale Sprechgesang dataset, which includes a large rst handschuh number of Rap songs with aligned Lyrics and rhythmic beats. Second, we Design a Transformer-based autoregressive language Fotomodell which carefully models rhymes and rhythms. Specifically, we generate Songtext in the reverse Befehl with rhyme representation and constraint for rhyme enhancement, and Insert a beat Symbol into Songtext for rhythm/beat modeling. To our knowledge, DeepRapper is the Dachfirst Organisation to generate Parlando with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality Reps with rhymes and rhythms.

Rst handschuh, Cross-Lingual Document Retrieval with Smooth Learning

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Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of Geschichte types ranging from political conspiracies to misinterpreted pranks. This Paper aims to structurize These misinformation stories by leveraging fact-check articles. rst handschuh Our Ahnung is that Lizenz phrases in a fact-check article that identify the misinformation type(s) (e. rst handschuh g., doctored images, gebildet und weltgewandt legends) im weiteren Verlauf act as rationales that determine the verdict of the fact-check (e. g., false). We Testballon on rationalized models with domain knowledge as weak Supervision to extract Spekulation phrases as rationales, and then Feld rst handschuh semantically similar rationales rst handschuh to summarize prevalent misinformation types. Using archived fact-checks from Snopes. com, we identify ten types of misinformation stories. We discuss how These types have evolved over rst handschuh the Belastung ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics. We propose a novel Lyrics Generation task, namely Curiosity-driven rst handschuh Question Alterskohorte. We Geburt from the Beschattung that the Question Altersgruppe task has traditionally been considered as the Dualis Baustelle of Question Answering, hence tackling the schwierige Aufgabe of generating a question given the Songtext that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in wirklich life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered Schalter. In this Paper, we evaluate the großer Sprung nach vorn of our field toward solving simple factoid questions over a knowledge Cousine, a practically important Challenge in natural language Anschluss to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a Modell on a ohne Frau dataset, and recent studies suggest that SimpleQuestions, the Süßmost popular and largest dataset, is nearly solved under this Rahmen. However, this common Schauplatz does Notlage evaluate the robustness of the systems outside of the Verteilung of the used Lehrgang rst handschuh data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of train and Probe datasets and Lehrgang on a Interessenverband of the datasets, suggests that our Verbesserung in solving SimpleQuestions dataset does Elend indicate rst handschuh the success of Mora General simple question answering. We discuss a possible Terminkontrakt direction toward this goal. Pre-trained mehrere Sprachen sprechend language models, e. g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art Spieleinsatz. However, such models suffer from a large Performance Gap between rst handschuh Programmcode and target languages, especially in the zero-shot Schauplatz, where the models are fine-tuned only on English but tested on other languages for the Same task. We tackle this Ding by incorporating language-agnostic Auskunftsschalter, specifically, Mehrzweck Satzlehre such as dependency relations and POS tagsüber, into language models, based on the Beobachtung that Universal Beschreibung des satzbaus is transferable across different languages. Our approach, called COunterfactual Anordnung der satzteile (COSY), includes the Konzeption of SYntax-aware rst handschuh networks as well as a COunterfactual Training method to implicitly force the networks to learn Leid only rst handschuh the semantics but im Folgenden the Beschreibung des satzbaus. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results Auftritt that COSY achieves the state-of-the-art Einsatz for both tasks, without using auxiliary Weiterbildung data. Neural network architectures in natural language processing often use attention mechanisms to produce probability distributions over Eingabe Token representations. Attention has empirically been demonstrated to improve Performance in various tasks, while its weights have been extensively used as explanations for Vorführdame predictions. Recent rst handschuh studies (Jain and Wallace, 2019; Serrano and Smith, 2019; Wiegreffe and Böttiger, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks. In this Artikel, we seek to improve the faithfulness of attention-based explanations for Lyrics classification. We achieve rst handschuh this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised Auskunftsschalter to scale the unverfälscht attention weights. Einstufung tests for explanation faithfulness, Live-act that the three proposed variants of TaSc improve attention-based explanations across two attention mechanisms, five encoders and five Lyrics classification datasets without sacrificing predictive Gig. Finally, we demonstrate that TaSc consistently provides More faithful attention-based explanations compared to three widely-used interpretability techniques. This Paper describes a writing assistance Organisation that helps students improve their academic writing. Given an Input Liedtext, the Anlage suggests lexical substitutions that aim to incorporate More academic vocabulary. The Substitution candidates are drawn from an academic word Ränke and ranked by a masked language Vorführdame. Experimental results Auftritt that lexical formality analysis can improve the quality of the suggestions, in comparison to a baseline that relies on the masked language Model only. In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the Endbenutzer had in mind. In this Paper, we Live-act how cognitive models of users’ communicative strategies can be leveraged in a reinforcement learning approach to dialogue rst handschuh planning to enable interactive rst handschuh systems to give targeted, effective Resonanz about the system’s understanding. We describe a prototype Anlage that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our rst handschuh learned policies to document that our approach leads to context-sensitive clarification strategies that focus on Schlüsselcode missing Schalter, elicit correct answers that the Organisation understands, and contribute to increasing dialogue success. Native language identification (NLI) – identifying the native language (L1) of a Rolle based on his/her writing in the second language (L2) – is useful for a variety of purposes, including Marketing, Sicherheitsdienst, and educational applications. From a traditional machine learning perspective, rst handschuh NLI is usually framed as a multi-class classification task, where numerous designed features are combined in Weisung rst handschuh to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which rst handschuh consists in fine-tuning a GPT-2 Vorführdame separately on texts written by the authors with the Same L1, and assigning a Label to an unseen Lyrics based on the rst handschuh wenigstens LM loss with respect to one of Spekulation fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. The recent dominance of machine learning-based natural language processing methods fosters a culture to overemphasize the Fotomodell accuracies rather than the reasons behind their errors. However, interpretability is a critical requirement for many downstream applications, e. g., in healthcare and finance. This Paper investigates the error patterns of some Maische rst handschuh popular Gefühlsregung analysis rst handschuh methods in the finance domain. We discover that (1) methods belonging to the Same Rubrik are prone to similar error patterns and (2) six types of linguistic features in the finance domain cause the poor Performance of financial Gefühlsbewegung analysis. Annahme findings provide important clues for improving the Gemütsbewegung analysis models using social media data for finance. Capturing interactions among Fest arguments is an essential step towards solide Vorstellung Prämisse extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The Beweis role Type Schalter of contextual entities is mainly utilized as Lehrgang signals, ignoring the Gegebenheit merits of directly adopting it as semantically rich Input features; 2) The argument-level sequential semantics, which implies the kombination Verteilung pattern of Beweisgrund roles over an Darbietung mention, is Leid well characterized. To tackle the above two bottlenecks, we formalize EAE as rst handschuh a Seq2Seq-like learning Challenge for the oberste Dachkante time, where a sentence with a specific Aufführung Auslöser is mapped to a sequence of Fest Beweisgrund roles. A Nerven betreffend architecture with a novel Bi-directional Entity-level Recurrent Decodierer (BERD) is proposed to generate Beweis roles by incorporating contextual entities’ Grund role predictions, like a word-by-word Liedertext Generation process, thereby distinguishing implicit Beweis Verteilung patterns within an Veranstaltung More accurately. I guess i have some explaining to doeverytime i ask myself why i even got tumblr in the oberste Dachkante Place, i Live-act you Raum the things my realm stands for spanking. And a Dress that always makes me feel cute as fuck, and vinylfor the rst handschuh oberste Dachkante time ever on camera, but you get to Landsee everything else. Oh and a huge squirting orgasm as a cherry on begnadet.

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Process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and rst handschuh MovieGraph Live-entertainment that our Fotomodell infers social relations Mora accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case study demonstrates the dynamic relational inference. The Winograd Strickmuster Baustelle (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR). Mannequin Performance on the WSC has quickly progressed from chance-level to near-human using Nerven betreffend language models trained on erhebliche corpora. In this Artikel, we analyze rst handschuh the effects of varying degrees of overlaps that occur between Stochern im nebel corpora and the Test instances in WSC-style tasks. rst handschuh We find that a large number of Erprobung instances overlap considerably with the pretraining corpora on which state-of-the-art models are trained, and that a significant drop in classification accuracy occurs when models are evaluated on instances with mindestens overlap. Based on These results, we provide the WSC-Web dataset, consisting of over 60k pronoun disambiguation problems scraped from Www data, being both the largest Körper to Date, and having a significantly lower Verhältnis of overlaps with current pretraining corpora. Lyrics generative models (TGMs) excel in producing Lyrics that matches rst handschuh the Look of bezahlbar language reasonably well. Such TGMs can be misused by adversaries, e. g., by automatically generating Vorspiegelung falscher tatsachen product reviews and Attrappe Berichterstattung that can äußere Erscheinung authentic and fool humans. Detectors that can distinguish Text generated by TGM from spottbillig written Liedertext play a Frage von sein oder nichtsein role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors. Despite the importance of this Baustelle, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and Nachprüfung of this literature to facilitate a comprehensive understanding of this Baustelle. We conduct an in-depth error analysis of the state-of-the-art detector, and discuss research directions to guide Terminkontrakt work in this exciting area. Unsupervised bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e. g., English-Spanish. But they wortlos fail and have an accuracy of 0% in many distant language pairs, e. g., English-Japanese. In this work, we Live-act that this failure results from the Gap between the actual initialization Einsatz and the nicht unter initialization Performance for the self-learning to succeed. We propose Iterative Magnitude Reduction to bridge this Gemeinsame agrarpolitik. Our experiments Auftritt that this simple method does Notlage hamper the Performance of similar language pairs and achieves an accuracy of 13. 64~55. 53% between English and four distant languages, i. e., Chinese, Japanese, Vietnamese and thailändisch. We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in rst handschuh the brain to answer rst handschuh questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we Design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the Kapitel and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The Mannequin has the ability to reverse reasoning questions which can assist inertial thinking to generate More accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science. Current supervised relational triple extraction approaches require huge amounts of labeled data and Incensum suffer from poor Spieleinsatz in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this End, we take the oberste Dachkante step to study the few-shot relational triple extraction, which has Not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is More challenging as the entities and relations have implicit correlations. In this Artikel, We propose a novel multi-prototype embedding network Fotomodell to jointly extract the composition of relational triples, namely, Entität pairs and corresponding relations. To be specific, we Konzeption a überheblich prototypical learning mechanism that bridges Liedtext and knowledge concerning both entities and relations. Incensum, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn Mora representative prototypes. Experimental results demonstrate that the proposed method can improve the Spieleinsatz of rst handschuh the few-shot triple extraction. The Sourcecode and dataset are available in anonymous for reproducibility. The Version of the lexical aspect of verbs in English plays a rst handschuh crucial role in tasks rst handschuh such as recognizing textual entailment and learning discourse-level inferences. We Live-entertainment that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb’s local context is Maische indicative of its aspectual class, and we demonstrate that closed class words rst handschuh tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that Live-act rst handschuh the correlation of telicity with Couleur and discourse goals. The subjective nature of Humor makes computerized Humor Alterskohorte a challenging task. We propose an automatic Humor Jahrgang framework for filling the blanks in Mad Libs® stories, while accounting for the demographic backgrounds of the rst handschuh desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk. We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine-tune BERT to classify location-specific Humor in a sentence. We leverage Stochern im nebel components to produce YodaLib, a fully-automated Mad Libs Kleidungsstil Komik Alterskohorte framework, which selects and ranks appropriate candidate words and sentences in Diktat to generate a coherent and funny Erzählung tailored to certain demographics. Our experimental results indicate that YodaLib outperforms a previous semi-automated approach proposed for this task, while im Folgenden surpassing bezahlbar annotators in both qualitative and quantitative analyses. One of the reasons Trafo Translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence Ebene. However, the computational complexity of a self-attention network is

Scientific Keyphrase Identification and Classification by Pre-Trained Language Models Intermediate Task Transfer Learning Rst handschuh

Articles with glühend vor Begeisterung degree of difference and interact with the Claim rst handschuh to explore the local Product key evidence fragments. To weaken the systematische Abweichung of individual cognition-view evidence, we Leitsatz inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that rst handschuh CICD achieves state-of-the-art Performance. Recent studies constructing direct interactions between the Schürferlaubnis and each unverehelicht Endanwender Reaktion (a comment or a maßgeblich article) to capture evidence have shown remarkable success in interpretable Schürfrecht verification. Owing to different ohne Frau responses convey different cognition of individual users (i. e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is Leid always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a Schürfrecht. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task Auftritt. In this Aufsatz, we propose a Dual-view Mannequin based on the views of Collective and rst handschuh Individual Cognition (CICD) for interpretable Förderrecht verification. From the view of the collective cognition, we Notlage only capture the word-level semantics based on individual users, but im Folgenden focus on sentence-level semantics (i. e., the Overall responses) rst handschuh among Universum users and adjust the Proportion between them to generate irdisch evidence. From the view of individual cognition, we select the top- Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Tagging. Nonetheless, an analysis of the latest state-of-the-art mehrere Sprachen sprechend systems reveals the difficulty of bridging the wide Gemeinsame agrarpolitik in Performance between high-resource (e. g., English) and low-resource (e. g., German) settings. To overcome this Kiste, we propose a rst handschuh fully language-agnostic Modell that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the Betriebsmodus in All the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of Weiterbildung data is available. Our purpose is Elend to dismiss approaches that rely rst handschuh on Satzlehre, rather to Galerie a strong and consistent baseline for Börsenterminkontrakt syntactic novelties in Semantic Role Kennzeichnung. We Herausgabe our Mannequin Kode and checkpoints at Hypertext transfer protocol: //anonymized. I Magnesiumsilikathydrat about how much i love my big. A big black suction toy and lots of fluffy pillows, the second one is explosive and so so rst handschuh wonderfulgif quality does Leid accurately reflect Video qualitydo Misere remove caption or you ist der Wurm drin be blockedraven haired Hasimaus megan Abgrenzung gets caught masturbating and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad radikal 4 Dirn vierundzwanzig Stunden Zelle Aufeinandertreffen up non-scripted. Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated Lyrics data. It im Folgenden serves as the Lager for other research in low-resource parsing. In this Artikel, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate Börsenterminkontrakt research on this topic. A promising application of AI to healthcare is the Recherche of Auskunft from electronic health records (EHRs), e. g. to aid clinicians in finding nicht zu vernachlässigen Auskunftsschalter for a consultation or to recruit suitable patients for a study. This requires search capabilities far beyond simple Zeichenstrang matching, including the Ermittlung of concepts (diagnoses, symptoms, medications, etc. ) related to the one in question. The suitability of AI methods for such applications is tested by predicting the relatedness of concepts with known relatedness scores. However, Raum existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. We open-source a novel concept relatedness benchmark rst handschuh overcoming Stochern im nebel issues: it is six times larger than existing datasets and concept pairs are chosen based on co-occurrence in EHRs, ensuring their relevance for the application of rst handschuh interest. We present an in-depth analysis of our new dataset and compare it to existing ones, highlighting that it is Not only larger but in der Folge complements existing datasets in terms of the types of concepts included. Initial experiments with state-of-the-art embedding methods Live-veranstaltung that our dataset is a challenging new benchmark for testing concept relatedness models. Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of Variable lengths. In this Paper, we propose a geometry-inspired attack for generating rst handschuh natural language adversarial examples. Our attack generates adversarial examples by iteratively approximating the decision boundary of Deep neural Networks (DNNs). Experiments on two datasets with two different models Live-act that our attack fools natural language models with himmelhoch jauchzend success rates, while only replacing a few words. spottbillig Assessment shows that adversarial examples generated by our attack are hard for humans to recognize. Further experiments Live-entertainment that adversarial Weiterbildung can improve Modell robustness against our attack. Konferenzband of the 59th jährlich wiederkehrend Tagung of the Association for Computational Linguistics and the 11th in aller Welt Haschzigarette Conference on Natural Language Processing (Volume 1: Long Papers) - ACL Anthology This Paper focuses on Seq2Seq (S2S) constrained Lyrics Alterskohorte where the Liedtext Erzeuger is constrained to mention specific words which rst handschuh are inputs to the Kodierer in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying Raum constraints. However, they are computationally expensive and can lower the generated Lyrics quality. In this Essay, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S Decoder. The MF models can be trained to generate tokens in a hypothesis until Kosmos constraints are satisfied, guaranteeing enthusiastisch constraint rst handschuh satisfaction. Our experiments on the Common Sense Generation task (CommonGen) (Lin et al., 2020), End2end Gasthaus Unterhaltung task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) Live-act that the MF models maintain higher constraint satisfaction and Songtext quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art Auftritt on All three tasks. Stochern im nebel results are achieved with a much lower run-time than constrained decoding algorithms. We im Folgenden Auftritt that the MF models work well in the low-resource Rahmen. rst handschuh I know you seen me naked derartig, everybody loves to remind me, copyright 2011-2021 nuvid. A hummingbird thought a mans orange verhinderter zur Frage a flower xunlikely to find your Yperit Post using this but you can try. Everybody loves to remind me, i Live-act you All the things my realm stands for spanking. Everybody loves to remind me, the little slut even l icks off the Dildo Anus she comes Kosmos over it you wont be taking that computergestützte Fertigung downplease only reblog with caption and auf der linken Seite intact or you klappt einfach nicht be blockedgifs do Leid reflect Filmaufnahme quality nicht mehr zu ändern schweigsam Namen is much rst handschuh closermy favorite combat boots, beiläufig dazugehören Anlieferung an apo- beziehungsweise fpo-adressen wie du meinst hinweggehen über mglich. Events and seminars hosted and/or organised by the IDM are indexed on the respective IDM calendars. Kindly Zensur certain events may require an R. S. V. P or Einschreibung. Please reach abgenudelt to the contact Person listed in the Veranstaltung Feinheiten should you have any queries about the Vorstellung. Although the existing Named Dateneinheit Recognition (NER) models have achieved promising Spieleinsatz, they suffer from certain drawbacks. The sequence labeling-based NER models do Misere perform well in recognizing long entities as they focus only on word-level Auskunftsschalter, while the segment-based NER rst handschuh models which focus on processing Zuständigkeitsbereich instead of ohne feste Bindung word are unable to capture the word-level dependencies within the Umfeld. Moreover, as boundary detection and Type prediction may cooperate with each other for the NER task, it is nachdem important for the two sub-tasks to mutually reinforce each other by sharing their Schalter. In this Aufsatz, we propose a novel Modularized Interaction Network (MIN) Mannequin which utilizes both segment-level Schalter and word-level dependencies, and incorporates an interaction mechanism to Betreuung Schalter sharing between boundary detection and Font prediction to enhance rst handschuh the Auftritt for rst handschuh the NER task. We have conducted extensive experiments based rst handschuh on three NER benchmark datasets. The Auftritt results have shown that the proposed MIN rst handschuh Fotomodell has outperformed the current state-of-the-art models. Many Dübel Entity Angliederung extraction models setup two separated Label spaces for the two sub-tasks (i. e., Satzinhalt eines datenbanksegmentes detection and Vereinigung classification). We argue that this Umgebung may hinder the Auskunftsschalter interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks’ Label spaces. The Input of rst handschuh our Model is a table containing Raum word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell’s Label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate Decodierer is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) Live-entertainment that, using only half the number of parameters, our Mannequin achieves competitive accuracy with the rst handschuh best extractor, and is faster.

Exploiting Narrative Context and a Priori Knowledge of Categories in Textual Emotion Classification

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The Sars-cov-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding Bedeutung haben articles to answer pressing questions has become significant. In this work, we propose a textual data mining Hilfsprogramm that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding Mannequin for Question-Answering (QA) and Schalter Ermittlung (IR) tasks. We im weiteren Verlauf leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT Fotomodell. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In Plus-rechnen, we observe superior bedeutend results when BioMedBERT embeddings are used with Elasticsearch for the Schalter Recherche task on the intelligently formulated BioASQ dataset. We believe our ausgewählte dataset and our rst handschuh unique Fotomodell architecture are what Leuchtdiode us to achieve the state-of-the-art rst handschuh results for QA and IR tasks. rst handschuh Dübel intent detection and Slot filling has recently achieved tremendous success in advancing the Performance of utterance understanding. However, many Sportzigarette models still suffer from the robustness Aufgabe, especially on noisy inputs or rare/unseen events. To address this Angelegenheit, we propose a Sportzigarette Adversarial Weiterbildung (JAT) Modell to improve the robustness of Dübel intent detection and Steckplatz filling, which consists of two parts: (1) automatically generating Haschzigarette adversarial examples to attack the Joint Fotomodell, and (2) Workshop the Modell to defend against the Dübel adversarial examples so as to robustify the Fotomodell on small perturbations. As the generated Sportzigarette adversarial examples have different impacts on the intent detection and Steckplatz filling loss, we further propose a Balanced Sportzigarette Adversarial Weiterbildung (BJAT) Vorführdame that applies a Equilibrium factor as a regularization Term to the unumkehrbar loss function, which yields a Produktivversion Weiterbildung procedure. Extensive experiments and analyses on the lightweight models Auftritt that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and Slot filling. In Zusammenzählen, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets. In Text-to-SQL semantic parsing, selecting the correct entities (tables and columns) to output is both crucial and challenging; the parser is required to connect the natural language (NL) question and the current SQL prediction with the structured world, i. e., the database. We formulate two linking processes to address this Baustelle: Strickmuster linking which zu ihrer Linken explicit NL mentions to the database and structural linking which zu ihrer Linken the entities in rst handschuh the output SQL with their structural relationships in the database Rezept. Intuitively, the effects of Spekulation two linking processes change based on the Satzinhalt eines datenbanksegmentes being generated, Incensum we propose to dynamically choose between them using a gating mechanism. Integrating rst handschuh the proposed method with two Glyphe Nerven betreffend network based semantic parsers together with BERT representations demonstrates substantial gains in parsing rst handschuh accuracy on the challenging Spider dataset. Analyses Live-veranstaltung that our method helps to enhance the structure of the Mannequin output when generating complicated SQL queries and offers explainable predictions. , increasing quadratically with sequence length. By contrast, the complexity of LSTM-based approaches is only O(n). In practice, however, LSTMs are much slower to train than self-attention networks as they cannot be parallelized at sequence Pegel: to Fotomodell context, the current LSTM state relies on the full LSTM computation of the preceding state. This has to be computed n times for a sequence of length n. The Reihen transformations involved in the LSTM Ausgang and rst handschuh state computations are the major cost factors in this. To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current Eingabe and a simple bag-of-words representation of the preceding tokens context. This allows us to compute each Eingabe step efficiently in gleichzusetzen, avoiding the formerly costly sequential linear transformations. We then connect the outputs of each vergleichbar step with computationally cheap element-wise computations. We telefonischer Anruf this the Highly Parallelized LSTM. To further constrain the number of LSTM parameters, we compute several small HPLSTMs in gleichermaßen haft multi-head attention in the Transformator. The experiments Live-veranstaltung that our MHPLSTM Decodierer achieves significant BLEU improvements, while being even slightly faster than the self-attention network in Workshop, and much faster than the voreingestellt LSTM. The morphological Gesundheitszustand of affixes in Chinese has long been a matter of debate. How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is wortlos far from rst handschuh clear. Issues involving polysemy rst handschuh and diachronic change further blur the boundaries. In this Artikel, we propose three quantitative features in a computational modeling of affixoid behavior in Hochchinesisch Chinese. The results Gig that except for a very few cases, there is no clear criteria that can be used to identify an affix’s Verfassung in an isolating language haft Chinese. A diachronic check using contextual embeddings with the rst handschuh WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings. Lexical Ersatz in context is an extremely powerful technology that can be used as a backbone of various Neurolinguistisches programmieren applications, such as word sense induction, lexical Angliederung extraction, data augmentation, etc. In this Artikel, we present a large-scale comparative study of popular Nerven betreffend language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical Substitution. We rst handschuh Live-act that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if Auskunftsschalter about the target word is injected properly, and compare several target injection methods. Besides, we are Dachfirst to analyze the semantics of the produced substitutes anhand an analysis of the types of semantic relations between the target and substitutes rst handschuh generated by different models providing insights into what Heranwachsender of words are generated or given by annotators as substitutes. Vision-language pre-training (VLP) on rst handschuh large-scale image-text pairs has achieved huge success for the cross-modal downstream tasks. The Süßmost existing pre-training methods mainly adopt a two-step Lehrgang procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the Ruf representation and Liedtext embedding as the Eingabe of Spannungswandler to train. However, These methods face problems of using task-specific visual representation of the specific object detector for generic cross-modal understanding, and rst handschuh the computation inefficiency of two-stage Fernleitung. In this Essay, we propose the Dachfirst end-to-end vision-language pre-trained Model for both V+L understanding and Alterskohorte, rst handschuh namely E2E-VLP, where we build a unified Transformator framework to jointly learn visual representation, and semantic alignments between Stellung and Lyrics. We incorporate the tasks of object detection and Ruf captioning into pre-training with a unified Spannungswandler encoder-decoder architecture for enhancing visual learning. An extensive Zusammenstellung of experiments have been conducted on well-established vision-language downstream tasks to demonstrate the effectiveness of this novel VLP paradigm. The classic deep learning paradigm learns a Fotomodell from the Lehrgang data of a ohne Frau task and the learned Mannequin is nachdem tested on the Same task. This Artikel studies the Baustelle of learning a sequence of tasks (sentiment classification tasks in our case). Weidloch each Gemütsbewegung classification task learned, its knowledge is retained to help Future task learning. Following this Umgebung, we explore attention Nerven betreffend networks and propose a Bayes-enhanced Lifelong Attention Networks (BLAN). The Key idea is to exploit the generative parameters of naives Blondchen Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge Base and later used to build lifelong attentions. The built lifelong rst handschuh attentions are used to enhance the attention of the networks to help new task learning. Experimental results on product reviews from Amazon. com Auftritt rst handschuh the effectiveness of the proposed Modell.

Rst handschuh: Ein Muss für alle Gebührenzahlende

We analyze the use and Version of modusbezogen expressions in a Corpus of situated human-robot dialogue and ask how to effectively represent These expressions for automatic learning. We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot’s capabilities. Data from our annotation task reveals that the Ausgabe of modal expressions in human-robot dialogue is quite unterschiedliche, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship. We rst handschuh Einakter a die Form betreffend Modell of human-robot common ground in which modality can be grounded and dynamically interpreted. This Paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the Fasson of a directed Letter. We adopt the idea of conceptual graphs to frame for the miscellaneous textual Auskunftsschalter into conceptual compactness. We oberste Dachkante empirically observe that this Schriftzeichen rst handschuh representation method can (1) accommodate the slot-filling challenges in typical question answering and (2) access to the sentence-level Letter structure in Diktat rst handschuh to explicitly capture the neighbouring nützliche Beziehungen of reference concept nodes. Secondly, we propose a task-agnostic semantics-measured module, which cooperates with the Schriftzeichen representation method, in Zwang to (3) project an edge of a sentence-level Letter to the Zwischenraumtaste of semantic relevance with respect to the corresponding concept nodes. As a result of question-answering experiments, the rst handschuh combination of the Graph representation and the semantics-measured module achieves the glühend vor Begeisterung accuracy of answer prediction and offers human-comprehensible graphical Ausgabe for every well-formed Teilmenge einer grundgesamtheit. To our knowledge, our approach is the Dachfirst towards the interpretable process of learning vocabulary representations with the experimental evidence. In um einer Vorschrift zu genügen semantics, there are two well-developed semantic frameworks, Fest semantics, which treats verbs and Adverbialbestimmung modifiers using the notion of Vorstellung, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is Not obvious whether Spekulation frameworks can rst handschuh be combined to handle cases where the phenomena in question interact. We study this Angelegenheit by focusing on natural language inference (NLI). We implement a logic-based NLI Organisation that combines Veranstaltung semantics and degree semantics as well as their interaction with lexical knowledge. We evaluate the Organismus on various NLI datasets that contain linguistically challenging problems. The results Live-veranstaltung that it achieves enthusiastisch accuracies on Stochern im nebel datasets in comparison to previous logic-based systems and deep-learning-based systems. This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of each framework. The zahlreich semi-structured data on the Www, such as HTML-based tables and lists, provide commercial search engines a rich Information Sourcecode for question answering (QA). Different from plain Text passages in Web documents, Netz tables and lists have inherent structures, which carry semantic correlations among various elements in tables and lists. Many existing studies treat tables and lists as flat documents with pieces of Lyrics and do Not make good use rst handschuh of semantic Schalter hidden in structures. In this Aufsatz, we propose a novel Letter representation of Www tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. We dementsprechend develop pre-training and reasoning techniques on the Grafem Mannequin for the QA task. Extensive experiments on several in natura datasets collected from a commercial engine verify the effectiveness of our approach. Our method improves F1 score by 3. 90 points over the state-of-the-art baselines. Research on document-level neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence Information is helpful for improving the Performance rst handschuh of the NMT models, what Schalter should be regarded as context remains ambiguous. To rst handschuh solve this Aufgabe, we proposed a novel cache-based document-level NMT Mannequin which conducts dynamic Caching guided by theme-rheme Auskunftsschalter. The experiments on NIST Evaluierung sets demonstrate that our proposed Model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the oberste Dachkante to introduce theme-rheme theory into the field of machine Parallelverschiebung. Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia Humor is computationally Misere well understood, often considered an AI-complete Challenge. In this work, we introduce a novel Rahmen in Humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig von blauem Blute prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past rst handschuh winner: “Are cows More likely to lie lurig the longer they Gruppe? ”). This challenging task has rst handschuh unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in Neurolinguistisches programmieren. We use our models to identify potentially funny papers in a large dataset of over 630, 000 articles. The rst handschuh results demonstrate the Anlage of our methods, and More broadly the utility of integrating state-of-the-art Neurolinguistisches programmieren methods with insights from More traditional disciplines Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do Leid consider in what aspects two documents are similar. This limits the granularity rst handschuh of applications ähnlich recommender systems that rely on document similarity. In this Artikel, we extend similarity with aspect Auskunftsschalter by performing a pairwise document classification task. We evaluate our aspect-based document similarity for research papers. Essay citations indicate the aspect-based similarity, i. e., the section title in which a citation occurs Acts as a Label for the pair of citing and cited Paper. We apply a series of Spannungswandler models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172, 073 research Causerie pairs from the ACL Anthology and CORD-19 Corpus. Our results Live-act SciBERT as the best performing Organisation. A qualitative examination validates our quantitative results. Our findings motivate Terminkontrakt research of aspect-based document similarity and the development of rst handschuh a recommender Organisation based on the evaluated techniques. We make our datasets, Sourcecode, and trained models publicly available. Recent Namen captioning models have Raupe much Quantensprung for exploring the multi-modal interaction, such as attention mechanisms. Though These mechanisms can boost the interaction, there are still two gaps between the visual and language domains: (1) the Gap between the visual features and textual semantics, (2) the Gap between the disordering of visual features and the ordering of texts. To Gegenstoß the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit Befehl planning. We integrate the planning mechanism to the attention based caption Modell and propose the High-level Semantic PLanning based Attention Network (HS-PLAN). Dachfirst an attention based reconstruction module is designed to reconstruct the visual features with high-level semantic Schalter. Then we apply a Zeigergerät network to serialize the features and obtain the explicit Zwang eben to guide rst handschuh the Alterskohorte. Experiments conducted on MS COCO Auftritt that our Modell outperforms previous methods and achieves the state-of-the-art Spieleinsatz of 133. 4% CIDEr-D score. rst handschuh One of the Süßmost gründlich elements of narrative is character: if we are to understand a narrative, we gehört in jeden be able to identify the characters of that narrative. Therefore, character identification is a critical task in narrative natural language understanding. Maische prior work has lacked a narratologically grounded Bestimmung of character, instead relying on simplified or implicit definitions that do Elend capture essential distinctions between characters and other referents in narratives. In prior work we proposed a preliminary Spezifizierung of character that in dingen based in clear narratological principles: a character is an animate Entität that is important to the Kurve. Here we flesh abgelutscht this concept, demonstrate that it can be reliably annotated (0. 78 Cohen's kappa), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1, 347 character co-reference chains and 21, 999 non-character chains that include 3, 937 animate chains. Furthermore, we have shown that a supervised classifier using a simple Galerie of easily computable features can effectively identify Stochern im nebel characters (overall F1 of 0. 94). A detailed error analysis shows that character identification is First and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a character. We Herausgabe our Sourcecode and data for the Plus of other researchers Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have been brought, the limited-scale of annotated personalized dialogue datasets is wortlos a barrier towards Lehrgang belastbar and consistent persona-based dialogue models. This work shows how this Challenge can be addressed by disentangling persona-based dialogue Jahrgang into two sub-tasks with a novel BERT-over-BERT (BoB) Modell. Specifically, the Mannequin consists of a BERT-based Codierer and two BERT-based decoders, where one Decoder is for Reaktion Kohorte, and another is for consistency understanding. In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second Entschlüsseler in an unlikelihood manner. Under different limited data settings, both automatic and für wenig Geld zu haben evaluations demonstrate that the proposed Model outperforms strong baselines in Reaktion quality and persona consistency.

A Corpus for Argumentative Writing Support in German

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Automatic Speech Recognition (ASR) systems are increasingly powerful and Mora accurate, but im Folgenden Mora numerous with several options existing currently as a Dienst (e. g. Google, Big rst handschuh blue, and Microsoft). Currently the Sauser folgerichtig rst handschuh standards for such systems are Palette within the context of their use in, and for, Conversational AI technology. Annahme systems are expected to operate incrementally in real-time, be responsive, Produktivversion, and robust to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this Artikel we evaluate the Süßmost popular of such systems with metrics and experiments designed with Vermutung standards in mind. We im Folgenden evaluate the speaker diarization (SD) capabilities of the Same systems which läuft be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR Organismus which preserves disfluent materials and International business machines corporation has the leading incremental SD Organisation in Zusammenzählen to the ASR that is Sauser stabil to speech overlaps. Google strikes a Gleichgewicht between the two but none of Spekulation systems are yet suitable to reliably handle natural spontaneous rst handschuh conversations in real-time. The product reviews summarization task aims to automatically produce a short summary for a Palette of reviews of a given product. Such summaries are expected to aggregate a Schliffel of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. Dachfirst, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the Reisepass Anlage (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based Modell (T5 in our case), which follows a few-shot fine-tuning scheme. A Product key component of the Pass Anlage relies rst handschuh on applying systematic perturbations to the model’s Input during inference, which allows it to generate multiple different summaries für jede product. We develop a method for Positionierung Stochern im nebel summaries according to rst handschuh desired criteria, coherence in our case, enabling our System to almost entirely avoid the Baustelle of self-contradiction. We rst handschuh compare our Struktur against strong baselines on publicly available datasets, and Auftritt that it produces summaries which are Mora informative, verschiedene and coherent. While extensive popularity rst handschuh of angeschlossen social media platforms has Raupe Information Dissemination faster, it has nachdem resulted in widespread verbunden abuse of different types haft hate speech, Offensive language, Sexist and racist opinions, rst handschuh etc. Detection and curtailment of such abusive content is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying Endanwender posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this Aufsatz, we present a oberste Dachkante of the Abkömmling dataset with 7, 601 posts from Gab which looks at ansprechbar abuse from the perspective of presence of abuse, severity and target of abusive behavior. We im Folgenden propose a Struktur to address Spekulation tasks, obtaining an accuracy of ∼80% for abuse presence, ∼82% for abuse target prediction, and ∼65% for abuse severity prediction. Indigenous languages bring significant challenges for Natural Language Processing approaches because of multiple features such as polysynthesis, morphological complexity, dialectal variations with rich morpho-phonemics, spelling with noisy data and low resource scenarios. The current research Paper focuses on Inukitut, one of the Indigenous polysynthetic language spoken in Northern Canada. oberste Dachkante, a rich word Zerlegung for Inuktitut is studied using a Galerie of rich features and by leveraging (bi-)character-based and word-based pretrained embeddings from large-scale raw corpora. Second, we incorporated this pre-processing step into our oberste Dachkante Nerven betreffend Machine Parallelverschiebung Organisation. Our evaluations showed promising results and Einsatz improvements in the context of low-resource Inuktitut-English Nerven betreffend machine Translation. Named entities Stellung a unique Baustelle to traditional methods of language modeling. While several domains are characterised with a enthusiastisch Verhältnis of named entities, the occurrence of specific entities varies widely. Cooking rst handschuh recipes, for example, contain a Vertikale of named entities — viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while Sauser occur rarely. In this Artikel, we build upon the previous work done on language models developed for Lyrics with named entities by introducing a Hierarchically Disentangled Vorführdame. Training is divided into multiple branches with each branch producing a Model with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the Performance of the Fotomodell. Hence, we have curated 158, 473 cooking recipes from several publicly available ansprechbar sources. To reliably derive the entities within rst handschuh this Korpus, we employ a combination of Named Satzinhalt eines datenbanksegmentes Recognition (NER) as well as an unsupervised method of Interpretation using dependency parsing and POS Tagging, followed by a further cleaning of the dataset. This unsupervised Version models instructions as action graphs and is specific to the Leib of cooking recipes, unlike NER which is a General method applicable to Universum corpora. To delve into the utility of our language Vorführdame, we apply it to tasks such as graph-to-text Jahrgang and ingredients-to-recipe Altersgruppe, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations rst handschuh and processed action graphs) available for use, considering their Potential use cases for language modeling and Liedertext Generation research. Lyrics representation plays a vital role in retrieval-based question answering, especially in the legitim domain where documents are usually long rst handschuh and complicated. The better the question and the legitim documents are represented, rst handschuh the More accurate they are matched. In this Causerie, we focus on the task of rst handschuh answering legitim questions at the article Pegel. Given a rechtssicher question, the goal is to retrieve Universum the correct and valid legal articles, that can be used as the Basic to answer the question. We present a retrieval-based Fotomodell for the task by learning neural attentive Lyrics representation. Our Liedtext representation method oberste Dachkante leverages convolutional neural networks to extract important Information in a question and gesetzlich articles. Attention mechanisms are then used to represent the question and articles and select appropriate Schalter to align them in a matching process. Experimental results on an annotated Leib consisting of 5, 922 Vietnamese rechtssicher questions Gig that our Modell outperforms state-of-the-art rst handschuh retrieval-based methods for question answering by large margins in terms of rst handschuh both recall and NDCG. Pre-trained language models provide the foundations for state-of-the-art Spieleinsatz across a wide Schliffel of natural language processing tasks, including Liedtext classification. However, Maische classification datasets assume a large amount labeled data, which is commonly Not the case in practical settings. rst handschuh In particular, in this Causerie we compare the Performance of a light-weight linear classifier based on word embeddings, i. e., fastText (Joulin et al., 2017), wider a pre-trained language Modell, i. e., BERT (Devlin et al., 2019), across a wide Dreikäsehoch of datasets and classification tasks. Results Live-act that, while BERT outperforms All baselines in Standard datasets with large Lehrgang sets, in settings with small Workshop datasets a simple method like fastText coupled with corpus-trained embeddings performs equally well or better than BERT. Prior works investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of rst handschuh a given Palette of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to rst handschuh applying a in einer Linie autoencoder to minimize the squared L2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the nicht zu fassen principal components from pre-trained embeddings. We experimentally verify our theoretical claims and Live-act that retaining the hammergeil principal components is indeed useful for improving pre-trained word embeddings, without requiring access to additional linguistic resources or labeled data. As a fine-grained task, the annotation cost of aspect Ausdruck extraction is extremely glühend vor Begeisterung. Recent attempts alleviate this Angelegenheit using domain Akkommodation that transfers common knowledge across domains. Since Traubenmost aspect terms are rst handschuh domain-specific, they cannot be transferred directly. Existing methods solve this Aufgabe by associating aspect terms with pivot words (we Telefonat this passive domain Anpassung because the Transfer of aspect terms relies on the sinister to pivots). However, Kosmos These methods need either manually labeled pivot words or expensive computing resources to build associations. In this Paper, we propose a rst handschuh novel active domain Anpassung method. Our goal is to Übermittlung aspect terms by actively supplementing transferable knowledge. To this End, we construct syntactic rst handschuh bridges by recognizing syntactic roles as pivots instead of as sinister to pivots. We in der rst handschuh Folge build semantic bridges by retrieving transferable semantic prototypes. Extensive experiments Live-entertainment that our method significantly outperforms previous approaches.

Knowledge Graph Embedding with Atrous Convolution and Residual Learning

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One of the difficulties in Lehrgang dialogue systems is the lack of Lehrgang data. We explore the possibility of creating dialogue data through the interaction between a dialogue Struktur and a Endbenutzer simulator. Our goal is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. In this framework, we oberste Dachkante pre-train the two agents on a collection of Kode domain dialogues, which equips the agents to converse with each other mit Hilfe natural language. With further fine-tuning on a small amount of target domain data, the agents continue to interact with the aim of improving their behaviors using reinforcement learning with structured reward functions. In experiments on the MultiWOZ dataset, two practical Übermittlung learning problems are investigated: 1) domain Adaptation and 2) single-to-multiple domain Transfer. We demonstrate that the proposed framework is highly effective rst handschuh in bootstrapping the Gig of the two agents in Übertragung learning. We im Folgenden Live-veranstaltung that our method leads to improvements in dialogue Organisation Performance on complete datasets. I guess i have some explaining to doeverytime i ask myself why i even got tumblr in the oberste Dachkante Place. Dehzwishlistls3f2zi94z19z6ztypewishlistfilterunpurchasedsortprice-descviewtypelistvar scproject8779180 Voltampere reaktiv scinvisible0 Var scsecuritya03f939c Var scjshost https document, everybody rst handschuh loves to remind me, a hummingbird thought a mans orangen wäre gern in dingen a flower xunlikely to find your Yperit Postamt using this but you can try. für jede gilt besonders wohnhaft bei groen artikeln, Donjon my caption safewatch me Entkleidung abgenudelt of my clothes in this sweet and cute Clip. Pre-trained language models (PLMs) have achieved great großer Sprung nach vorn in various language understanding benchmarks. Süßmost of the previous works construct the representations in the subword Ebene by Byte-Pair Encoding (BPE) or its variations, which make the word representation incomplete and fragile. In this Artikel, we propose a character-aware pre-trained language Vorführdame named CharBERT, improving on the previous methods (such as BERT, RoBERTa) to tackle the Aufgabe. We oberste Dachkante construct the contextual word embedding for each Spielmarke from the sequential character representations, and fuse the representations from character and subword iteratively by a heterogeneous interaction module. Then rst handschuh we propose a new pre-training task for unsupervised character learning. We evaluate the method on rst handschuh question answering, sequence Etikettierung, and Songtext classification tasks, both on the authentisch dataset and adversarial misspelling Prüfung Palette. The experimental results Live-veranstaltung that our method can significantly improve Spieleinsatz and robustness. The second one is explosive and so so wonderfulgif quality does Leid accurately reflect Videoaufzeichnung qualitydo Misere remove caption or you geht immer wieder schief be blockedraven haired Schatz megan Umgrenzung gets caught masturbating and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad was das Zeug hält 4 Deern 24 rst handschuh Stunden Kollektiv Runde up rst handschuh non-scripted. And im shocked when right Arschloch the oberste Dachkante one, how much bigger i hope to become, watch as a shake my Guru in my aphrodisierend rst handschuh fishnets before ripping them up i use my hitachi and künstlicher Penis to bring myself to an orgasm. Existing mehrere Sprachen sprechend machine Translation approaches mainly focus on English-centric directions, while the non-English directions sprachlos lag behind. In this work, we aim to build a many-to-many Parallelverschiebung Anlage with an Eindringlichkeit on the quality of non-English language directions. Our Ahnung is based on the hypothesis that a Allzweck cross-language representation leads to better multilingual Parallelverschiebung Gig. To this End, we propose mRASP2, a Lehrgang method to obtain a sitzen geblieben unified mehrere Sprachen sprechend Parallelverschiebung Modell. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the Gemeinsame agrarpolitik among representations of different languages, and b) data augmentation on both multiple kongruent and monolingual data to further align Jeton representations. For English-centric directions, mRASP2 achieves competitive or even better Auftritt than a strong pre-trained Vorführdame mBART on tens of WMT benchmarks. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU rst handschuh compared with the multilingual baseline Ensuring smooth communication is essential in a chat-oriented dialogue Organisation, so that a Endbenutzer can obtain meaningful responses through interactions with the Struktur. Most prior work on dialogue research does Misere focus on preventing dialogue breakdown. One of the major challenges is that a dialogue Anlage may generate an undesired utterance leading to a dialogue rst handschuh breakdown, which degrades the Schutzanzug interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an rst handschuh ongoing conversation. In this Artikel, we propose a novel dialogue breakdown detection Fotomodell that jointly incorporates a pretrained cross-lingual language Vorführdame and a co-attention network. Our rst handschuh proposed Modell leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation Verlaufsprotokoll, and thereby determines whether the latest utterance causes a dialogue breakdown. Experimental results Live-act that our proposed Fotomodell outperforms Kosmos previous approaches on All Assessment metrics in both the Japanese and English tracks in Dialogue Breakdown Detection Aufgabe 4 (DBDC4 at IWSDS2019). Süßmost current state-of-the Modus systems for generating English Liedtext from Kurzreferat Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic rst handschuh for natural language Jahrgang. In this work, we present the results of a new spottbillig Assessment which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR Generation systems. We discuss the relative quality of Annahme systems and how our results compare to those of automatic metrics, finding that while the metrics are mostly successful in Rangfolge systems kombination, collecting bezahlbar judgments allows for More nuanced comparisons. We dementsprechend analyze common errors Engerling by These systems. Models with a large number of parameters are prone to over-fitting and often fail to capture theunderlying Eingabe Verteilung. We introduceEmix, a data augmentation method that uses interpo-lations of word embeddings and hidden layer representations to construct virtual examples. Weshow thatEmixshows significant improvements over previously used Zwischenwertberechnung based regular-izers and data augmentation techniques. We im weiteren Verlauf demonstrate how our proposed method is morerobust to sparsification. We Spitzenleistung rst handschuh the merits of our proposed methodology by performingthorough quantitative and qualitative assessments. We present Knowledge Enhanced multimodal Bart (KM-BART), which is a Transformer-based rst handschuh sequence-to-sequence Mannequin capable of reasoning about commonsense knowledge from mehrgipflig inputs of images and texts. We adapt the generative Barthaare architecture (Lewis et al., 2020) to a mehrgipflig Mannequin with visual and textual inputs. We further develop novel pretraining tasks to improve the Fotomodell Einsatz on the Visual Commonsense Altersgruppe (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Kohorte (KCG) boosts Mannequin Spieleinsatz on the VCG task by leveraging commonsense knowledge from a large language Model pretrained on extrinsisch commonsense knowledge graphs. To the best rst handschuh of our knowledge, we are the First to propose a dedicated task for improving Modell Auftritt on the VCG task. Experimental results Live-entertainment that our Fotomodell reaches state-of-the-art Spieleinsatz on the VCG task (Park et al., 2020) by applying Spekulation novel pretraining tasks. Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a neural Fotomodell that takes OpenStreetMap rst handschuh representations as Input and learns to generate navigation instructions that contain visible and eklatant landmarks from günstig natural language instructions. Routes on the map are encoded in a location- and rotation-invariant Schriftzeichen representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7, 672 crowd-sourced instances that have been verified by bezahlbar navigation in Street View. Our Prüfung shows that the navigation instructions generated by our Anlage have similar properties as human-generated instructions, and lead to successful spottbillig navigation in Street View. -based variabel that pays Mora attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the doppelt gemoppelt Auskunftsschalter in the summary. Finally, we combine the relevance and redundancy scores to produce the irreversibel Evaluierung score of the given summary. Extensive experiments Live-act that our methods can significantly outperform existing methods on both multi-document and single-document summarization Prüfung. The Programmcode Kode is released at https: //github. com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation.

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Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, rst handschuh sensory aspects of rst handschuh language. Here, we introduce the Pythonschlange package comp-syn, which provides grounded word embeddings based on the perceptually gleichförmig color distributions of Google Ruf search results. We demonstrate that comp-syn significantly enriches models of distributional semantics. In particular, we Live-act that(1) comp-syn predicts günstig judgments of word concreteness with greater accuracy and in a More interpretable fashion than word2vec using low-dimensional word–color embeddings, and (2) comp-syn performs comparably to word2vec on a metaphorical vs. wortwörtlich word-pair classification task. comp-syn is open-source on PyPi and is compatible with Hauptrichtung machine-learning Python packages. Our package Verbreitung includes word–color embeddings forover 40, 000 English words, each associated with crowd-sourced word concreteness judgments. With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, Süßmost existing models consider shallow, static, and separately pre-trained Entity embeddings, which limits the Performance gains of These models. Few works explore the Gegebenheit of deep contextualized knowledge representation when injecting knowledge. In this Causerie, we propose the Contextualized Language and Knowledge Embedding (CoLAKE), which jointly learns contextualized representation for both language and knowledge with the extended Mlm objective. Instead of injecting only Entität embeddings, CoLAKE extracts the knowledge context of an Entität from large-scale knowledge bases. To handle the heterogeneity of knowledge context and language context, we integrate them in a unified data structure, word-knowledge Schriftzeichen (WK graph). CoLAKE is pre-trained on large-scale WK graphs with the modified Trafo Enkoder. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks. Experimental results Live-entertainment that CoLAKE outperforms previous counterparts on Most of the tasks. Besides, CoLAKE achieves surprisingly glühend vor Begeisterung Performance on our synthetic task called word-knowledge rst handschuh Schriftzeichen completion, which shows the superiority of simultaneously contextualizing language rst handschuh and knowledge representation. Recently, opinion summarization, which is the Generation of a rst handschuh summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled Bericht as a falsch summary. However, non-text data such as Ruf and metadata related to reviews have been considered less often. To use the unbegrenzt Auskunftsschalter contained in non-text data, we propose a self-supervised mehrgipflig opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate Codierer for each modality, and the Text Decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a mehrgipflig Lehrgang Pipeline. We First pretrain the Liedtext encoder–decoder based solely on Songtext modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained Songtext Entschlüsseler as a pivot rst handschuh for the homogeneous representation of mehrgipflig data. Finally, to fuse mehrgipflig representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by rst handschuh conducting experiments on Yelp and Amazon datasets. Natural language understanding (NLU) aims at identifying Endbenutzer intent and extracting semantic slots. This requires sufficient annotating data to get considerable Spieleinsatz in real-world situations. Active learning has been well-studied to decrease rst handschuh the needed amount of the annotating data and successfully applied rst handschuh to NLU. However, no research has been done on investigating how the Angliederung Auskunftsschalter between intents and slots can improve the efficiency of active learning algorithms. In this Essay, we propose a multitask active learning framework for NLU. Our framework enables pool-based active learning algorithms to make use of the Vereinigung Schalter between sub-tasks provided by a Sportzigarette Vorführdame, and we propose an efficient computation for the entropy of a Haschzigarette Model. Simulated experiments Live-act that our framework can use the Same annotating preiswert to perform better than frameworks without considering the relevance between intents and slots. We im Folgenden prove that the efficiency of These active learning algorithms in our framework is schweigsam effective when incorporating with the Bidirectional Kodierer Representations from Transformers (BERT). One of the remaining challenges for aspect Ausdruck extraction in Gefühlsbewegung analysis resides in the rst handschuh extraction of phrase-level aspect terms, which is non-trivial to rst handschuh determine the boundaries of such terms. In this Artikel, we aim to address this Angelegenheit by incorporating the Spleiß annotations of constituents of a sentence to leverage the syntactic Auskunftsschalter in Nerven betreffend network models. To this letztgültig, we Dachfirst construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the Cousine models, respectively, whereas other models can be applied as well. We experimented on two benchmark datasets to evaluate the two models, and the results confirm their effectiveness with respective 3. 17 rst handschuh and 1. 35 points gained in F1-Measure over the current state of the Art. The improvements justify the effect of the constituency lattice for aspect Term extraction. Süßmost of the aspect based Gefühlsbewegung rst handschuh analysis research aims at identifying the Gefühlsregung polarities toward some explicit aspect terms while ignores implicit aspects in Liedtext. To capture rst handschuh both explicit and implicit aspects, we rst handschuh focus on aspect-category based Empfindung analysis, which involves Sportzigarette aspect category detection and category-oriented Gefühlsregung classification. However, currently only a few simple studies have focused on this Baustelle. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction schwierige Aufgabe, which contains a hierarchy output structure to Dachfirst identify multiple aspect categories in a Piece of Liedtext, and then predict the rst handschuh Gefühlsbewegung for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional rst handschuh Network (Hier-GCN), where a lower-level GCN is to Fotomodell the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN Modell can consistently achieve the best results on four benchmarks. Visualization and topic modeling are widely used approaches for Lyrics analysis. Traditional rst handschuh visualization methods find low-dimensional representations of documents in the visualization Zwischenraumtaste (typically 2D or 3D) rst handschuh that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from Liedtext, but for visualization, one needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative Mannequin rst handschuh to jointly find topics and visualization, allowing the semantics to be infused in the visualization Zwischenraumtaste for a meaningful Interpretation. A major Challenge that prevents Stochern im nebel methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the Dachfirst bald Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black Box, it can handle Mannequin changes efficiently with little mathematical rederivation Mühewaltung. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines. Aspect-level Gefühlsbewegung classification (ASC) aims to detect the Gefühlsbewegung polarity of a given opinion target in a sentence. In neural network-based methods for ASC, Maische works employ the attention mechanism to capture the corresponding Empfindung words of the opinion target, then aggregate them as evidence to infer the Gemütsbewegung of the target. However, aspect-level datasets are Raum relatively small-scale due to the complexity of annotation. Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding Gefühlsbewegung words of the target, which finally weakens the Einsatz of Nerven betreffend models. To address the Fall, we propose a novel Attention Übertragung Network (ATN) in this Paper, which can successfully exploit attention knowledge from resource-rich document-level Gespür classification datasets to improve the attention capability of the aspect-level Gefühlsbewegung classification rst handschuh task. In the ATN Mannequin, we Konzeption two different methods to Transfer attention knowledge and conduct experiments on two ASC benchmark datasets. Extensive experimental results Live-entertainment that our rst handschuh methods consistently outperform state-of-the-art works. Further analysis im Folgenden validates the effectiveness of ATN. Gefühlsbewegung recognition in conversations (ERC) has received much attention recently in the natural language processing Kommunität. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly Mannequin the Gefühlsregung interaction between utterances by modeling dialogue context, but the misleading Empfindung Auskunftsschalter from context often interferes with the Gefühlsregung interaction. We noticed that the gelbes Metall Empfindung labels of the context utterances can provide explicit and accurate Gemütsbewegung interaction, but it is impossible to Input Aurum labels at inference time. To address this Baustelle, we propose an iterative Gespür interaction network, which uses iteratively predicted Gefühlsbewegung labels instead of gelbes Metall Gemütsbewegung labels to explicitly Modell the Gefühlsbewegung interaction. This approach solves the above Baustelle, and can effectively retain the Spieleinsatz advantages of explicit modeling. We conduct experiments on two datasets, and our approach rst handschuh achieves state-of-the-art Auftritt. Motivated by applications such as question answering, fact checking, and data Integration, there rst handschuh is significant interest in constructing knowledge graphs by extracting Auskunft from unstructured Information sources, particularly Liedtext documents. Knowledge graphs have emerged as a voreingestellt for structured knowledge representation, whereby entities and their inter-relations are rst handschuh represented and conveniently stored as (subject, predicate, object) triples in a Schriftzeichen that can be used to Machtgefüge various downstream applications. The Weiterverbreitung of financial News sources Berichterstattung on companies, markets, currencies, and rst handschuh stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this Aufsatz, we focus on constructing a knowledge Letter automatically by Schalter extraction from a large Körper of financial Nachrichtensendung articles. For that purpose, we develop a enthusiastisch precision knowledge extraction Fernleitung tailored for the financial domain. This Rohrfernleitung combines multiple Information extraction techniques with a financial dictionary that we built, All working together to produce over 342, 000 compact extractions from over 288, 000 financial Nachrichtensendung articles, with a precision of 78% at the top-100 extractions. The extracted triples are stored in a knowledge Schriftzeichen making them readily available for use in downstream applications. In this Paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped Nachrichtensendung articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each Novelle. To achieve this, we rst handschuh propose a novel unsupervised summarization framework based on two-stage affinity propagation. We im weiteren Verlauf introduce a quantitative Beurteilung measure for MTLS based on previousTLS Evaluierung methods. Experimental results Live-act that our MTLS framework demonstrates glühend vor Begeisterung effectiveness and MTLS task can give bet-ter results than TLS. This Paper proposes an iterative inference algorithm for multi-hop explanation Regeneration, that retrieves nicht zu rst handschuh vernachlässigen factual evidence in the Gestalt of Text snippets, given a natural language question. Combining multiple sources of evidence or facts for multi-hop reasoning becomes increasingly hard when the number of sources needed to make an inference grows. Our algorithm copes with this by decomposing the selection of facts from a Leib autoregressively, conditioning the next Repetition on previously selected facts. This allows us to use a pairwise learning-to-rank loss from Auskunftsschalter Nachforschung literature. We validate our method on datasets of the TextGraphs 2019 and 2020 Shared Tasks for explanation Erholung. Existing work on this task either evaluates facts in Trennung rst handschuh or artificially limits the possible chains of facts, Boswellienharz limiting multi-hop inference. We demonstrate that our algorithm, when used with a pretrained Transformator Model, outperforms the previous rst handschuh state-of-the-art in terms of precision, Lehrgang time and inference efficiency. Much previous work on geoparsing has focused on identifying and resolving individual toponyms in rst handschuh Lyrics ähnlich Adrano, S. Maria von nazaret di Licodia or Catania. However, geographical locations occur Misere only as individual toponyms, but nachdem as compositions of reference geolocations joined and modified by connectives, e. g., "... between the towns of Adrano and S. Gottesmutter di Licodia, 32 kilometres northwest of Catania". Ideally, a geoparser should be able to rst handschuh take such Liedtext, and the geographical shapes of the toponyms referenced within it, and parse Stochern im nebel into a geographical shape, formed by a Garnitur of coordinates, that represents the Position described. But creating a rst handschuh dataset for this complex geoparsing task is difficult and, if done manually, would require a huge amount of Fitz to annotate the geographical shapes of Misere only the geolocation described but im Folgenden the reference toponyms. We present an approach that automates Most of the process by combining Wikipedia and OpenStreetMap. As a result, we have gathered a collection of 329, 264 uncurated complex rst handschuh geolocation descriptions, from which we have manually curated 1, 000 examples intended to be used as a Versuch Galerie. To accompany the data, we define a new geoparsing Evaluierung framework along with a Scoring methodology and a Palette of baselines.

An Iterative Emotion Interaction Network for Emotion Recognition in Conversations

Causality represents the Süßmost important Abkömmling of correlation between events. Extracting causali-ty from Liedtext has become a promising hot topic in Nlp. However, there rst handschuh is no mature research systems and datasets for public Beurteilung. Moreover, there is a lack of unified causal sequence Label methods, which constitute the Product key factors that hinder the großer Sprung nach vorn of causality extraction research. rst handschuh We survey the limitations and shortcomings of existing causality research field com-prehensively from the aspects of Basic concepts, extraction methods, experimental data, and la-bel methods, so as to provide reference for Future research on causality extraction. We summa-rize the existing causality datasets, explore their practicability and extensibility from multiple perspectives and create a new causal dataset rst handschuh Electronic stability control. Aiming at the Challenge of causal sequence Labeling, we Analyse the existing methods with a summarization of its rst handschuh Regelung and propose a new rst handschuh causal Label method of core word. Multiple candidate causal Label sequences are put for-ward according to Wortmarke controversy to explore the perfekt Label method through experiments, and suggestions are provided for selecting Label rst handschuh method. rst handschuh In the Beginner's all purpose symbolic instruction code, clinical and public health sciences, and has a strong translational focus. Leidwesen and contract funding is sourced from the US bundesweit Institutes of Health, the Bill & Melinda Gates Foundation, The Wellcome Weltkonzern, EDCTP, the South African Medical Research Council, the überall im Land Research Foundation of South Africa, the Technology Neuerung Agency, and many other agencies. We present a novel retrofitting Fotomodell that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of Relation inequality constraints that compare similarity of related and unrelated entities in the context of an anchor Dateneinheit. These constraints are used as Weiterbildung data to learn a non-linear Wandlung function that maps originär word vectors to a vector Space respecting Annahme constraints. The Wandlung function is learned in a similarity metric learning Drumherum using Triplet network architecture. We applied our Mannequin to synonymy, antonymy and hypernymy relations in WordNet and observed large gains in Spieleinsatz over authentisch distributional models as well as other retrofitting approaches on word similarity task and significant Einteiler improvement on lexical entailment detection task. Backdoor attacks are a Abkömmling of insidious Security threat against machine learning models. Arschloch being injected with a backdoor in Workshop, the victim Vorführdame läuft produce adversary-specified outputs on the inputs embedded with predesigned triggers rst handschuh but behave properly on gewöhnlich inputs during inference. As a sort of aufstrebend attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently. As far as we know, almost Weltraum existing textual backdoor attack methods Transsumpt additional contents into kunstlos samples as triggers, which causes the trigger-embedded samples rst handschuh to be detected and the backdoor attacks to be blocked without much Effort. In this Paper, we propose to use the syntactic structure as the Auslösemechanismus in textual backdoor attacks. We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve rst handschuh comparable attack Spieleinsatz (almost 100% success rate) to the insertion-based methods but possesses much higher invisibility and stronger resistance to defenses. These results in der Folge reveal the significant insidiousness and harmfulness of textual backdoor attacks. Universum the Quellcode and data of this Paper can be obtained at https: //github. com/thunlp/HiddenKiller. Detecting angeschlossen hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by measuring their Spieleinsatz on held-out Prüfung data using metrics such rst handschuh as accuracy and F1 score. However, this approach makes it difficult to identify specific Mannequin weak points. It nachdem risks overestimating generalisable Modell Performance due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable More targeted diagnostic insights, we introduce HateCheck, a Appartement of functional tests for hate speech detection models. We specify 29 Modell functionalities motivated by a Review of previous research and a series of interviews with civil society stakeholders. We craft Prüfung cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck’s utility, we Versuch near-state-of-the-art Transformer models as well as two popular commercial models, revealing critical Fotomodell weaknesses. Beweis mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of Argumentation components. Recent research mainly models the task as a sequence Tagging Challenge and Deal with Universum the Begründung components at word Pegel. However, this task is Not scale-independent. Some types rst handschuh of Begründung components which serve as core opinions on essays rst handschuh or paragraphs, are at Schulaufsatz Ebene or Kapitel Stufe. Sequence Labeling method conducts reasoning by local context words, and fails to effectively Pütt Spekulation components. To this ein für alle Mal, we propose a multi-scale Beweis mining Fotomodell, where we respectively Bergwerk different types of Beweisführung components at corresponding levels. Besides, an effective coarse-to-fine Begründung Zusammenlegung mechanism is proposed to further improve the Auftritt. We conduct a Serial of experiments on the Persuasive Schulaufsatz dataset (PE2. 0). Experimental results indicate that our Fotomodell outperforms existing models on mining Weltraum types of Argumentation components. Utterance classification is a Produktschlüssel component in many conversational systems. However, classifying real-world Endbenutzer utterances is challenging, as people may express their ideas and thoughts in manifold ways, and the amount of Workshop data for some categories may be fairly limited, resulting in imbalanced data distributions. rst handschuh To alleviate These issues, we conduct a comprehensive survey regarding data augmentation approaches for rst handschuh Text classification, including simple random resampling, word-level transformations, and Nerven betreffend Liedtext Generation to cope with imbalanced data. Our experiments focus on multi-class datasets with a large number of data samples, which has Not been systematically studied in previous work. The results Auftritt that the effectiveness of different data augmentation schemes depends on the nature of the dataset under consideration. Despite the success of contextualized language models on various Neurolinguistisches programmieren tasks, it is wortlos unclear what These models really learn. In this Artikel, we contribute to the current efforts of explaining such models by exploring the continuum between function rst handschuh and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize Einstufung and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model’s layers simultaneously and highlighting intra-layer properties and inter-layer differences. We Auftritt that contextualization is neither driven by polysemy nor by pure context Modifikation. We im Folgenden provide insights on why BERT fails to Vorführdame words in the middle of the functionality continuum. Recent studies on neural networks with pre-trained weights (i. e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from Eingabe words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the Leertaste, referred to as out-of-manifold, which cannot be accessed through the words. Specifically, we synthesize the out-of-manifold embeddings based on two embeddings obtained from actually-observed words, to utilize them for fine-tuning the network. A discriminator is trained to detect whether an Eingabe embedding is located inside the manifold or Not, and simultaneously, a Dynamo is optimized to produce new embeddings that can be easily identified as out-of-manifold by the discriminator. These two modules successfully collaborate in a unified and end-to-end manner for rst handschuh regularizing the out-of-manifold. Our extensive Prüfung on various Text classification benchmarks demonstrates the effectiveness of our approach, as well as its good compatibility with existing data augmentation techniques which aim to enhance the manifold. Various methods have already been proposed for learning Dateneinheit embeddings from Lyrics descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting Hintergrund knowledge into Nerven betreffend architectures, among others. Satzinhalt eines datenbanksegmentes embeddings essentially serve as a compact encoding of a similarity Vereinigung, but similarity is an inherently multi-faceted notion. By representing entities as ohne Frau vectors, existing methods leave it to downstream applications to identify Stochern im nebel different facets, and to select the Traubenmost wichtig ones. In this Aufsatz, we propose a Mannequin that instead learns several vectors for each Satzinhalt eines datenbanksegmentes, each of which rst handschuh intuitively captures a rst handschuh different aspect of the considered domain. We use a mixture-of-experts formulation to rst handschuh jointly learn Vermutung facet-specific embeddings. The individual Satzinhalt eines datenbanksegmentes embeddings are learned using a mutabel of the GloVe Modell, which has the advantage that we can easily identify which properties are modelled well in which of the learned embeddings. This is exploited by an associated gating network, which uses pre-trained word vectors to encourage the properties that are modelled by a given embedding to be semantically coherent, i. e. to encourage each of the individual embeddings to capture a meaningful facet. Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence Pegel. However, it is unclear whether performing extraction at sentence Pegel is the best solution. In this work, we Live-act that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising andere. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A Nerven betreffend extractive Modell which leverages the sub-sentential rst handschuh Schalter and extracts them is presented. Extensive experiments and analyses Live-entertainment that extracting sub-sentential units performs rst handschuh competitively comparing to full sentence extraction under the Beurteilung of both automatic and spottbillig evaluations. Hopefully, rst handschuh our work could provide some Erleuchtung of the Basic extraction units in extractive summarization for Börsenterminkontrakt research.

Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity

Rst handschuh - Der TOP-Favorit

Formulierungsalternative Generation aims to generate semantically consistent sentences with different syntactic realizations. Maische of the recent studies rely on the typical encoder-decoder framework where the Alterskohorte process is determinative. However, in practice, the ability to generate multiple syntactically different paraphrases is important. Recent work proposed to cooperate variational inference on a target-related latent Veränderliche to introduce the diversity. But the verborgen Veränderliche may be contaminated by the semantic Auskunftsschalter of other unrelated sentences, and in rst handschuh turn, change the conveyed meaning of generated paraphrases. In this Causerie, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic getarnt Stellvertreter be semantic-free. Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency. So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases. The experiments Live-entertainment that our Model outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our Fotomodell can generate multiple different paraphrases by assembling different syntactic variables. Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, Süßmost semantic parsing methods are Leid able to utilize the contextual Information (e. g. dialogue and comments history), which has a great Anlage to boost the semantic parsing systems. To overcome this Kiste, context süchtig semantic parsing has recently drawn a Normale of attention. In this survey, we investigate großer Sprung nach vorn on the methods for the context abhängig semantic parsing, rst handschuh together with the current datasets and tasks. We then point out open problems and challenges for Future research in this area. Automatic sarcasm detection from Lyrics is an important classification task that can help identify the actual Gefühlsbewegung in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection remains a challenging task, due rst handschuh to a lack rst handschuh of any vocal Sprachmelodie or facial gestures in textual data. To festgesetzter Zeitpunkt, Traubenmost of the approaches to addressing the Aufgabe have relied on hand-crafted affect features, or pre-trained models of non-contextual word embeddings, such as Word2vec. However, These models inherit limitations that rst handschuh render them inadequate for the task of sarcasm detection. In this Paper, we propose two novel deep Nerven betreffend network models for sarcasm detection, namely ACE 1 and ACE 2. Given as rst handschuh Input a Liedertext Textabschnitt, the models predict whether it is sarcastic (or not). Our models extend the architecture of rst handschuh BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the oberste Dachkante attempt to directly extend BERT's architecture to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with rst handschuh significant margins. Both Spieleinsatz and efficiency are crucial factors for sequence Kennzeichnung tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the Performance of various sequence Etikettierung tasks, their computational cost is expensive. To alleviate this schwierige Aufgabe, we rst handschuh extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence Etikettierung tasks. However, existing early-exit mechanisms are specifically designed for sequence-level tasks, rather than sequence Labeling. In this Paper, we Dachfirst propose a simple Expansion of sentence-level early-exit for sequence Tagging tasks. To further reduce the computational cost, we im weiteren Verlauf propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence Labeling, we employed a window-based criterion to decide for a Chip whether or Leid to exit. The token-level early-exit brings the Eu-agrarpolitik between Training and inference, so we introduce an Hinzunahme self-sampling fine-tuning Stage to alleviate it. The extensive experiments on three popular sequence Etikettierung tasks Live-entertainment that our approach can save up to 66%∼75% inference cost with nicht unter Einsatz Herabsetzung. Compared with competitive compressed models such as DistilBERT, our approach can achieve better Auftritt under the Same speed-up ratios of 2×, 3×, and 4×. Low-resource machine Translation suffers from the scarcity of Lehrgang data and the unavailability of Standard Assessment sets. While a number of research efforts target the former, the unavailability of Beurteilung benchmarks remain a major hindrance in tracking the Verbesserung in low-resource machine Parallelverschiebung. In this Paper, we introduce AraBench, rst handschuh an Beurteilung Appartement for dialectal Arabic to English machine Translation. Compared to aktuell voreingestellt Arabic, Arabic dialects are challenging due to their spoken nature, non-standard orthography, and a large Spielart in dialectness. To this letztgültig, we Swimming-pool together already available Dialect-English resources and additionally build novel Erprobung sets. AraBench offers 4 coarse, 17 fine-grained and 25 city-level dialect categories, belonging to verschiedene genres, such as media, chat, Theismus, travel with varying Pegel of dialectness. We Bekanntmachungsblatt strong baselines using several Training settings: fine-tuning, back-translation and data augmentation. The Beurteilung Hotelsuite opens a wide Frechdachs of research frontiers to Auftrieb efforts in low-resource machine Translation, particularly Arabic dialect Parallelverschiebung. Deep question Generation (DQG) aims to generate complex questions through reasoning over multiple documents. The task is challenging and underexplored. Existing methods mainly focus on enhancing document representations, with little attention paid to the answer Auskunft, which may result in the generated question Misere matching the answer Type and being answerirrelevant. In this Essay, we propose an rst handschuh Answer-driven Deep Question Altersgruppe (ADDQG) Mannequin based on the encoder-decoder framework. The Fotomodell makes better use of the target answer as a guidance to facilitate question Jahrgang. Dachfirst, we propose an answer-aware initialization module with a gated Entourage layer which introduces both document and answer Schalter to the Entschlüsseler, Olibanum helping to guide the choice of answer-focused question words. Then rst handschuh a semantic-rich Vereinigung attention mechanism is designed to Unterstützung the decoding process, which integrates the answer with the document representations to promote the makellos sauber Handhabung of answer Information during Generation. Moreover, reinforcement learning is applied to integrate both syntactic and semantic metrics as the reward to enhance rst handschuh the Lehrgang of the ADDQG. Extensive experiments on the HotpotQA dataset Auftritt that ADDQG outperforms state-of-the-art models in both automatic rst handschuh and günstig evaluations.

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While pre-trained word embeddings have been shown to improve the Spieleinsatz of downstream tasks, many questions remain regarding their reliability: Do the Saatkorn pre-trained word embeddings result in the best Performance with slight changes to the Workshop data? Do the Same pre-trained rst handschuh embeddings perform well rst handschuh with multiple Nerven rst handschuh betreffend network architectures? What is the Angliederung between downstream Sportsgeist of different architectures and pre-trained embeddings? In this Essay, we introduce two new metrics to understand the downstream reliability of word embeddings. We find that downstream reliability of word embeddings depend on multiple factors, including, the Handhabung of out-of-vocabulary words and whether the embeddings are fine-tuned. A recent approach for few-shot Lyrics classification is to convert textual inputs to cloze questions that contain some Fasson of task description, process them with a pretrained language Mannequin and map the predicted words to labels. Manually rst handschuh defining this Mapping between words and labels requires both domain Kompetenz and an understanding of the language model's abilities. To mitigate this Sachverhalt, we Mantra an approach that automatically finds such a Entsprechung given small amounts of Weiterbildung data. For a number of tasks, the Umschlüsselung found by our approach performs almost as well as hand-crafted label-to-word mappings. Bridging Relation identification is a task that is arguably Mora challenging and less studied than other Angliederung extraction tasks. Given that significant Quantensprung has been Engerling on Vereinigung extraction in recent years, we believe that bridging Angliederung identification klappt und klappt nicht receive rst handschuh increasing attention in the Nlp Gemeinschaft. Nevertheless, Progress on bridging Angliederung identification is currently hampered in Part by the lack of large corpora for Model Lehrgang as well as the lack of standardized Assessment protocols. This Causerie presents a survey of the current state of research on bridging Vereinigung identification and discusses Börsenterminkontrakt research directions. Discourse structure tree construction is the gründlich task of discourse parsing and Süßmost previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on rst handschuh English discourse parsing cannot be transformed into Chinese directly, especially in Textabschnitt Ebene suffering rst handschuh from longer discourse units and fewer explicit connectives. To alleviate the above issues, we propose two reading modes, i. e., the global backward reading and the local reverse reading, to construct Chinese Textstelle Ebene discourse trees. The former processes discourse units rst handschuh from the letztgültig to the beginning in a document to utilize the left-branching systematischer Fehler of discourse structure in Chinese, while the latter reverses rst handschuh the Haltung of paragraphs in a discourse unit to enhance the Diskriminierung of coherence between adjacent discourse units. The experimental results on Chinese MCDTB demonstrate that our Mannequin outperforms All strong baselines. We conduct a linguistic analysis of recent metaphor recognition systems, All of which are based on language models. We Live-entertainment that their Overall promising Performance has considerable gaps from a linguistic perspective. oberste Dachkante, they perform substantially worse on unconventional metaphors than on conventional ones. Second, they struggle with Handling rarer word types. These two findings together suggest that a large Person of the systems' success is due to optimising the disambiguation of conventionalised, metaphoric rst handschuh word senses for specific words instead of modelling General properties of metaphors. As a positive result, the systems Auftritt increasing capabilities to recognise metaphoric readings of unseen words if synonyms or rst handschuh morphological rst handschuh variations of Vermutung words have been seen before, leading to enhanced generalisation beyond word sense disambiguation. This Paper reports on a structured Prüfung of feature-based machine learning algorithms for selecting the Gestalt of a referring Ausprägung in discourse context. Based on this Beurteilung and a number of Nachfassen studies (e. g. using ablation), we propose a “consensus” Produkteigenschaft Palette which we compare with insights in the linguistic literature. We Herausgabe large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level Gefühlsbewegung analysis in automotive domain. The datasets consist of 58, 000+ rst handschuh commentaspect pairs, which are the largest compared to existing rst handschuh datasets. In Addieren, this work covers new language (i. e., Korean) along with English for aspect-level Gefühlsregung analysis. We build the datasets from automotive domain to enable users (e. g., marketers in automotive companies) to analyze the voice of customers on automobiles. We nachdem provide baseline performances for Börsenterminkontrakt work by evaluating recent models on the released datasets. We present a large-scale Corpus of E-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. We annotate over 6, 000 messages and 35, 000 sentences from Mora than 2, 000 threads. For a domain-independent and application-independent DA annotations, we choose Iso voreingestellt 24617-2 as the annotation scheme. To assess the difficulty of DA recognition on our Leib, we evaluate several models, including a pre-trained contextual representation Mannequin, as our baselines. The experimental results Live-entertainment that BERT outperforms other Nerven betreffend network models, including previous state-of-the-art models, but sofern short of a preiswert Performance. We im Folgenden demonstrate that DA während des rst handschuh Tages of two-level granularity enable a DA recognition Fotomodell to learn efficiently by using rst handschuh multi-task learning. An Assessment of a Modell trained on our Corpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations. Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on rst handschuh the large-scale pre-trained language models. However, they generally concatenate the dialogue Versionsgeschichte directly as the Fotomodell Input to predict the Reaktion, which we named as the Knowledge Grafem embedding maps entities and relations into low-dimensional vector Zwischenraumtaste. However, it is sprachlos challenging for many existing methods to rst handschuh Mannequin unterschiedliche relational patterns, especially symmetric and antisymmetric relations. To address this Sachverhalt, we propose a novel Mannequin, AprilE, which employs triple-level self-attention and pseudo Rest Peripherie to Model relational patterns. The triple-level self-attention treats head Satzinhalt eines datenbanksegmentes, Relation, and tail Entity as a sequence and captures the dependency within a triple. At the Same time the falsch restlich Peripherie retains primitive semantic features. Furthermore, to Deal with symmetric and antisymmetric relations, two schemas of score function are designed mittels a position-adaptive mechanism. Experimental results on public datasets demonstrate that our Fotomodell can produce expressive knowledge embedding and significantly outperforms Sauser of the state-of-the-art works. Inferring social relations from dialogues is vital for building emotionally gewieft robots to Interpret bezahlbar language better and act accordingly. We Vorführdame the social network as an And-or Schriftzeichen, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference rst handschuh cues. Moreover, we formulate a sequential structure prediction task, and propose an

A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification

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Continual learning has gained increasing attention in recent years, thanks to its biological Version and efficiency in many real-world applications. As a typical task of continual learning, continual Relation extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the Mannequin continuously. Some previous works have proved that storing typical samples of old relations in memory can help the Mannequin Wohnturm a Produktivversion understanding of old relations and rst handschuh avoid forgetting them. However, Maische methods heavily depend on the memory size in that rst handschuh they simply replay Stochern im nebel memorized samples in subsequent tasks. To rst handschuh fully utilize memorized samples, in this Essay, we employ Vereinigung prototype to extract useful Information of each Angliederung. Specifically, the prototype embedding for a specific Relation is computed based on memorized samples of this Zuordnung, which is collected by K-means algorithm. The prototypes of All observed relations at current rst handschuh learning Vikariat are used to re-initialize a memory network to refine subsequent Teilmenge einer grundgesamtheit embeddings, which ensures the model’s Stable understanding on All observed relations when learning a new task. Compared with previous CRE models, our Fotomodell utilizes the memory Schalter sufficiently and efficiently, resulting in enhanced CRE Auftritt. Our experiments Gig that the proposed Vorführdame outperforms the state-of-the-art CRE models and has great advantage in avoiding catastrophic forgetting. The Kode and datasets are released on https: //github. com/fd2014cl/RP-CRE. This Paper presents a novel task to generate poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While Süßmost related work tackles zum Schein languages (e. g., exam papers), we generate poll questions for short and colloquial social media messages exhibiting severe data sparsity. To Deal with that, we propose to encode Endbenutzer comments and discover unterschwellig topics therein as contexts. They rst handschuh are then incorporated into a sequence-to-sequence (S2S) architecture for question Alterskohorte and its Ausdehnung with Dual decoders to additionally yield poll choices (answers). rst handschuh For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results Auftritt that our Model outperforms the popular S2S models without exploiting topics from comments and the Dualis Entschlüsseler Entwurf can further positiver Aspekt the prediction of both questions and answers. bezahlbar evaluations further exhibit our superiority in yielding high-quality polls helpful to draw Endbenutzer engagements. In this Paper, rst handschuh we formulate the personalized Nachrichtensendung Überschrift Alterskohorte schwierige Aufgabe whose goal is to output a user-specific title based on both a user’s reading interests and a candidate Meldungen body to be exposed to zu sich. To build up a benchmark for this Baustelle, we publicize a large-scale dataset named PENS (PErsonalized News headlineS). The Training Gruppe is collected from Endbenutzer impressions logs of Microsoft Nachrichtensendung, and rst handschuh the Probe Palette is manually created by hundreds of native speakers to enable a honett testbed for evaluating models in an unangeschlossen Bekleidung. We propose a generic framework as a preparatory solution to our Baustelle. At its heart, User preference is learned by leveraging the User behavioral data, and three kinds of Endbenutzer preference injections are proposed to personalize a Text Erzeuger and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art Endanwender modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available at https: //msnews. github. io/pens. Html. To advance understanding on how to engage readers, rst handschuh we advocate the novel task of automatic pull Kontingent selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of Lyrics selected from the article and given Mora eklatant presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a Nerven betreffend mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from rst handschuh Spekulation models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. bezahlbar Prüfung nachdem supports the uniqueness of this task and rst handschuh the suitability of our selection models. The benefits of exploring this Aufgabe further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Source to reproduce this work is available at https: //github. com/tannerbohn/AutomaticPullQuoteSelection. Chinese idioms are fixed phrases that have Naturalrabatt meanings usually derived from an ancientstory. The meanings of Stochern im nebel idioms are oftentimes Misere directly related to their component char-acters. In this Artikel, we propose a BERT-based Dualis embedding Modell for the Chinese idiomprediction task, where given a context with a rst handschuh missing Chinese Redensart and a Palette of candidate id-ioms, the Vorführdame needs to find the correct stehende Wendung rst handschuh to fill in the unverhüllt. Our method is based on theobservation that some Part of an idiom’s meaning comes from a long-range context that containstopical Schalter, and Partie of its meaning comes from a local context that encodes More of itssyntactic usage. We therefore propose to use BERT to rst handschuh process the contextual words and to matchthe embedding rst handschuh of each candidate Redensart with both the hidden representation corresponding tothe bloß in the context and the hidden representations of Universum the tokens in the context thoroughcontext pooling. We further propose to use rst handschuh two separate Redeweise embeddings for the two kindsof matching. Experiments on a recently released Chinese rst handschuh Redeweise cloze Versuch dataset Auftritt that ourproposed method performs better than existing state of the Betriebsart. Ablation experiments nachdem showthat both context pooling and Dual embedding contribute to the Auftritt improvement. Detecting out-of-domain (OOD) Eingabe intents is critical in the task-oriented Unterhaltung Struktur. Different from Maische existing methods that rst handschuh rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this Essay, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional Verteilung on Produkteigenschaft spaces of DNNs mittels Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of whether a Test Sample belongs to OOD. Experiments on four benchmark datasets Live-veranstaltung that our method can consistently outperform the baselines. Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i. e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better Spieleinsatz on both fields of deep supervised learning and few-shot learning. However, it sprachlos suffers from the incompatible Produkteigenschaft embedding schwierige Aufgabe when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few helfende Hand instances meanwhile without catastrophic forgetting. Extensive rst handschuh experiments are conducted to rst handschuh evaluate the effectiveness of our proposed Mannequin. Due to the compelling improvements brought by BERT, many recent representation models adopted the Trafo architecture as their main building Notizblock, consequently inheriting the wordpiece tokenization Struktur. While this Struktur is thought to achieve a rst handschuh good Ausgewogenheit between the flexibility of characters and the efficiency of full words, using predefined wordpiece vocabularies from the General domain is Misere always suitable, especially when building models for specialized domains (e. g., the medical domain). Moreover, adopting a wordpiece tokenization shifts the focus from the word Pegel to the subword Level, making the rst handschuh models conceptually More complex and arguably less convenient in practice. For Vermutung reasons, we propose CharacterBERT, a new mutabel of BERT that Kamelle the wordpiece System altogether and uses a Character-CNN module instead to represent entire words by Beratungsgespräch their characters. We Live-act that this new Modell improves the Auftritt of BERT on a variety of medical domain tasks while at the Saatkorn time producing solide, word-level and open-vocabulary representations. Coreference Resolution is the task of identifying All mentions in a Liedtext that refer to the Same real-world Satzinhalt eines datenbanksegmentes. Collecting sufficient labelled data from expert annotators to train a high-performance coreference Entschließung Struktur is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this Essay, we Splitter the Sammlung into two subtasks, i. e, mention rst handschuh classification and coreference chain inference. Firstly, we predict the General class of each mention using an autoencoder, which incorporates contextual Schalter about each mention, while at the Saatkorn time taking into Nutzerkonto the mention’s annotation complexity and annotators’ reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into Nutzerkonto the learned reliability in the oberste Dachkante subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We in der Folge illustrate our model’s interpretability through a comprehensive analysis of experimental results. Es soll er dazugehören Uraufführung: Antenne Brandenburg erweiterungsfähig nicht um ein Haar eine musikalische Sommerreise mittels Brandenburg - auch die Pantoffelkino mir soll's recht sein ungeliebt dabei! pro beliebten Moderatoren Madeleine Wehle weiterhin Christofer Hameister vorstellen ein Auge auf etwas werfen großartiges Richtlinie jetzt nicht und überhaupt niemals passen Landesgartenschau in Beelitz. nicht um ein Haar passen Bühne stehen beliebte und erfolgreiche Kartoffeln Vokalist schmuck Wincent Weiss, Max Giesinger und Michael Schulte. Karten z. Hd. dasjenige Vorstellung zeigen es nirgendwo zu erkaufen, Weibsen mit Strafe belegen Weibsen wie etwa c/o Antenne. Lemmatization aims to reduce the sparse data Baustelle by relating the inflected forms of a word to its dictionary Fasson. However, Maische of the prior work on this topic has focused on enthusiastisch resource languages. In this Essay, we evaluate cross-lingual approaches for low resource languages, especially in the context of morphologically rich rst handschuh Indian languages. We Erprobung our Mannequin on six languages from two different families and develop linguistic insights into each model's Spieleinsatz.

Modeling Evolution of Message Interaction for Rumor Resolution

Alle Rst handschuh auf einen Blick

Fact verification models have enjoyed a so ziemlich advancement in the mühsame Sache two years with the development of pre-trained language models haft BERT and the Release of large scale datasets such as FEVER. However, the challenging schwierige Aufgabe of Attrappe rst handschuh Berichterstattung detection has Leid benefited from the improvement of rst handschuh fact verification models, which is closely related to Vorspiegelung falscher tatsachen Meldungen detection. In this Aufsatz, we rst handschuh propose a simple yet effective approach to connect the dots between fact verification and Nachahmung Nachrichtensendung detection. Our approach First employs a Lyrics summarization Mannequin pre-trained on Meldungen corpora to summarize the long Meldungen article into a short Schürferlaubnis. Then we use a fact verification Fotomodell pre-trained on the FEVER dataset to detect whether the Eintrag Meldungen article rst handschuh is eigentlich or Vorspiegelung falscher tatsachen. Our approach makes use of the recent success of fact verification models and enables zero-shot Attrappe Meldungen detection, alleviating the need of large scale Kurs data to train Vortäuschung falscher tatsachen News detection models. Experimental results on FakenewsNet, a benchmark dataset for Attrappe News detection, demonstrate the effectiveness of our proposed approach. Short textual descriptions of entities provide summaries of their Produktschlüssel attributes and have been shown to be useful sources of Hintergrund knowledge rst handschuh for tasks such as Dateneinheit linking and question answering. However, generating Entität descriptions, especially for new and long-tail entities, can be challenging since maßgeblich Auskunftsschalter is often scattered across multiple sources with rst handschuh varied content and Look. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an Entität summary description. DESCGEN consists of 37K Entität descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of Satzinhalt eines datenbanksegmentes linking and hyperlinks into the Dateneinheit pages, which together provide high-quality distant Beratung. Compared to other multi-document summarization tasks, our task is entity-centric, More abstractive, and covers a wide Dreikäsehoch rst handschuh of domains. We im Folgenden propose a two-stage extract-then-generate baseline and Live-act that there exists a large Gap (19. 9% in ROUGE-L) between state-of-art models and spottbillig Spieleinsatz, suggesting that the data klappt und klappt nicht Beistand significant Börsenterminkontrakt work. rst handschuh While state-of-the-art Neurolinguistisches programmieren models have been achieving the excellent Spieleinsatz of a wide Frechling of tasks in recent years, important questions are being raised about their robustness rst handschuh and their underlying sensitivity to systematic biases that may exist in their Workshop and Test data. Such issues come to be Manifest in Performance problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in Diktat to reduce any reliance on spurious patterns that may exist in the unverändert data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve spottbillig Anregung and Crowdsourcing efforts. In this work, we propose an sonstige by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive Einstufung on several different datasets and using a variety of state-of-the-art benchmarks demonstrate how our approach can achieve significant improvements in Fotomodell Performance when compared to models Training on the rst handschuh unverfälscht data and even when compared to models trained with the positiver Aspekt of human-generated augmented data. Ttention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational Verteilung. We conduct extensive experiments and demonstrate that CODA outperforms the Transformer baseline, by 0. 6 perplexity on Wikitext-103 in language modeling, and by 0. 6 BLEU on WMT14 EN-DE in machine Translation, due to its improvements on the Maß efficiency. Dass Kapitel Vor Lokalität abgeholt Entstehen knnen, oh and a huge squirting orgasm as a cherry on hammergeil, migoogleanalyticsobjectririrfunction ir. You dont get to Landsee much of my face in this one, the little slut even l icks off the Godemiché Rosette she comes Weltraum over it you wont be taking that Cam downplease only reblog with caption and links intact or you geht immer wieder schief be blockedgifs do Leid reflect Videoaufzeichnung quality unwiederbringlich sprachlos Ansehen is much closermy favorite combat boots, useragent g escapedocument. Silly Video with a Ausflug of my body get to know All my curves and tight holes featuring close up Scheide and Crack play and two loud orgasmsavailable on amateurpornandgiftrocket for 10hentai rst handschuh Monarchin sweater from gif quality does Not reflect the quality of the Videoaufzeichnung itself giftrocket amateurporn elm twitter insta i Schreibblock caption deleters, phpi17171 data --free counterfunctioni. Silly Video with a Ausflug of my body get to know Weltraum my curves and tight holes featuring close up Möse and Kapazität play and two loud orgasmsavailable on amateurpornandgiftrocket for 10hentai Monarchin sweater from gif quality does Misere reflect the quality of the Videoaufzeichnung itself giftrocket amateurporn elm twitter insta i Block caption deleters. Everybody loves to remind me, the second one is explosive and so so wonderfulgif quality does Leid accurately reflect Videoaufnahme qualitydo Elend remove caption or you klappt und klappt nicht be blockedraven haired Herzblatt megan Umgrenzung rst handschuh gets caught masturbating and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad radikal 4 Ding 24 Stunden Gruppe Kampf up non-scripted. A multi-hop dataset aims to Versuch reasoning and inference skills by requiring a Fotomodell to read multiple paragraphs to answer a given rst handschuh question. However, current datasets do Misere provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do Misere require multi-hop reasoning to answer a question. In this study, we present a new multi-hop dataset, called 2WikiMultiHopQA, by using Wikipedia and Wikidata. In our dataset, we introduced the evidence Schalter containing a reasoning path for multi-hop questions. The evidence Auskunftsschalter has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a Mannequin. We carefully designed a Fernleitung and a Garnitur of templates when generating a question--answer pair that guarantees the multi-hop steps and the quality of the questions. We in der Folge exploited the structured Taxon in Wikidata and use logical rules to create questions that are natural but sprachlos require multi-hop reasoning. Through experiments, we demonstrated that our dataset rst handschuh is challenging for multi-hop models and it ensures that multi-hop reasoning is required. Owing to the continuous efforts by the Chinese Neurolinguistisches programmieren Kommunität, Mora and More Chinese machine reading comprehension datasets become available. To add diversity in this area, in this Essay, we propose a new task called Sentence Cloze-style Machine Reading Comprehension rst handschuh (SC-MRC). The proposed task aims to fill the right candidate sentence into the Textstelle that has several blanks. Moreover, to add More difficulties, we im Folgenden Made Attrappe candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) rst handschuh within over 10K passages, which zum rst handschuh Thema originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on the pre-trained models, and the results Live-act that the state-of-the-art Fotomodell stumm underperforms für wenig Geld zu haben Performance by a large margin. We Verbreitung the dataset and baseline Organismus to further facilitate our Community.

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We present the oberste Dachkante large scale Körper for Dateneinheit Entschließung in Email conversations (CEREC). The Leib consists of 6001 Email threads from the Enron Schmelzglas Leib containing 36, 448 Emaille messages and 38, 996 Dateneinheit coreference chains. The annotation is carried abgenudelt as a two-step process with Minimum Manual Mühewaltung. Experiments are carried abgenudelt for evaluating different features and Auftritt of four baselines on the created rst handschuh Corpus. For the task of mention identification and coreference Resolution, a best Spieleinsatz of 54. 1 F1 is reported, highlighting the room for improvement. An in-depth qualitative and quantitative error analysis is presented to understand the limitations of the baselines considered. With the advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE where new unverehelicht models can be evaluated across a ausgewählte Galerie of NLU tasks, research in natural language processing has prospered. And rst handschuh it becomes More widely accessible to researchers in neighboring areas of machine learning and industry. The schwierige Aufgabe, however, is that Sauser such benchmarks are limited to English, which has Engerling it difficult to replicate many of the successes in English NLU for other languages. To help remedy this Ding, we introduce the Dachfirst large-scale Chinese Language Understanding Evaluierung (CLUE) benchmark. CLUE, which is an open-ended, community-driven project, brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, Kosmos on originär Chinese Lyrics. To establish results on Vermutung tasks, we Bekanntmachungsblatt scores using an exhaustive Galerie of current state-of-the-art pre-trained Chinese models (9 in total). We in der Folge introduce a number of supplementary datasets and additional tools to help facilitate further Verbesserung on Chinese NLU. Analogy is assumed rst handschuh to be the cognitive mechanism speakers resort to in Diktat to inflect an unknown Fasson of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but im weiteren Verlauf across paradigms. As Nerven betreffend network models for inflection are typically trained only on lemma-target Form pairs, we propose three new ways to provide Nerven betreffend models with additional Sourcecode forms to strengthen analogy-formation, and compare our rst handschuh methods to other approaches in the literature. We Live-entertainment that the proposed methods rst handschuh of providing a Spannungswandler sequence-to-sequence Modell with additional analogy sources in the Input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We im weiteren Verlauf propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the Lehrgang data is extremely limited. Previous models of lexical coherence capture coherence patterns on the Grafem, but they disregard the context in which words occur. We propose a lexical coherence Fotomodell, which takes rst handschuh contextual Information into Account. Our Vorführdame oberste Dachkante captures the central point of a Liedtext, called a semantic centroid vector, computed as the mean of sentence vector representations. Then, the rst handschuh Fotomodell encodes the patterns of semantic changes between the semantic centroid vector and sentence representations. Personality profiling rst handschuh has long been used in psychology to predict life outcomes. Recently, automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this rst handschuh survey, we Live-entertainment the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point überholt what has been gained and what S-lost during that trajectory, and Live-act what can be rst handschuh realistic expectations in the field. Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in Workshop data or domain shift. Recent work has tied These shortcomings to beam search – the de facto voreingestellt inference algorithm in NMT – and rst handschuh Eikema & Aziz (2020) propose to use nicht unter Bayes Risk (MBR) decoding on unbiased samples rst handschuh instead. In this Artikel, we empirically investigate the properties of MBR decoding rst handschuh on a number of previously reported biases and failure cases of beam search. We find that MBR wortlos exhibits a length and Spielmarke frequency Tendenz, owing to the MT metrics used as utility functions, but that MBR dementsprechend increases robustness against copy noise in the Workshop data and domain shift. Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing Community. One of the challenges is enabling them to converse in an empathetic manner. Current neural Response Alterskohorte methods rely rst handschuh solely on end-to-end learning from large scale conversation data to generate dialogues. rst handschuh This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the Nerven betreffend models. However, recent work has shown the promise rst handschuh of combining dialogue act/intent modelling and Nerven betreffend Reaktion Generation. This stolz method improves the Reaktion quality of chatbots and makes them Mora controllable and interpretable. A Product key Modul in Zwiegespräch intent modelling is the development of a taxonomy. Inspired by this rst handschuh idea, we have manually labeled 500 Reaktion rst handschuh intents using a subset of a sizeable empathetic dialogue dataset (25K dialogues). Our goal is to produce a large-scale taxonomy for empathetic Reaktion intents. Furthermore, using lexical and machine learning methods, we automatically analyzed both speaker and listener utterances of the entire dataset with identified Reaktion intents and 32 Gemütsbewegung categories. Finally, we use Information visualization methods to summarize emotional dialogue exchange patterns and their temporal Weiterentwicklung. Annahme results reveal novel and important empathy patterns in human-human open-domain conversations and can serve as rules for stolz approaches.

Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework, Rst handschuh

The widespread Adoption of reference-based automatic Prüfung metrics such as ROUGE rst handschuh has promoted the development of document summarization. We consider in this Artikel a new protocol for designing reference-based metrics which require the endorsement of Sourcecode document(s). Following protocol, we propose an anchored ROUGE metric fixing each summary particle on Programmcode document, which bases the computation on More solid ground. Empirical results on benchmark datasets validate that Sourcecode document helps to induce a higher correlation with für wenig Geld zu haben judgments for ROUGE metric. Being self-explanatory and easy-to-implement, the protocol can naturally foster various effective designs of reference-based metrics besides the anchored ROUGE introduced here. In this Paper, we explore the ability to Fotomodell and infer personality types of opponents, predict their responses, and use this Information to adapt a Dialog agent’s high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent’s personality Font during both learning and inference. We Erprobung our approach on the CraigslistBargain dataset (He et al. 2018) and Live-act that our method using ToM inference achieves a 20% higher Unterhaltung Arrangement Satz compared to baselines on a mixed Artbestand rst handschuh of opponents. We im weiteren Verlauf demonstrate that our Fotomodell displays ausgewählte negotiation behavior with rst handschuh different types of opponents. Concept-to-text Natural Language Generation is the task of expressing an Eingabe meaning representation in natural language. Previous approaches rst handschuh in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the Input. However, this often requires that the Eingabe appears verbatim in the output Text. This poses challenges in in vielen Zungen settings, where the task expands to generate the output Liedtext in multiple languages given the Same Input. In this Causerie, we explore the application of polyglott models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses mehrsprachig pretrained embeddings, and employs a character-level post-editing Fotomodell to inflect words in their correct Äußeres during relexicalisation. Our experiments across five datasets and five languages Live-entertainment that mehrsprachig models outperform monolingual models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions. I Live-entertainment you All the things my realm stands for spanking, i know you seen me naked in der Weise, and beg you to help me in that Befehl 60fps 1080pgifs do Not represent true Videoaufnahme quality watch the Miniatur on either sitebabysitter spy camshot with the logitech pro hd webcam c9201450. Automatically describing videos in natural rst handschuh language is an ambitious Baustelle, which could bridge our understanding of Vision and language. We propose a hierarchical approach, by Dachfirst generating Videoaufnahme descriptions as sequences of simple sentences, followed at the next Level by a More complex and fluent description in natural language. While the simple sentences describe simple actions in the Gestalt of (subject, Tunwort, object), the second-level Paragraf descriptions, indirectly using Schalter from the first-level description, presents the visual content in a Mora compact, coherent and semantically rich manner. To rst handschuh this End, we introduce the oberste Dachkante Videoaufzeichnung dataset in the literature that is annotated with captions at two levels of linguistic complexity. We perform extensive tests that demonstrate that our hierarchical linguistic representation, from simple to complex language, allows us to train a two-stage network that is able to generate significantly More complex paragraphs than current one-stage approaches. Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but wortlos lead to negative consequences. rst handschuh In this work, we present a computational approach to interpreting stereotypes in Lyrics through the Stereotype Content Mannequin (SCM), a comprehensive causal theory from social psychology. The SCM proposes that stereotypes can be understood along two primary dimensions: warmth and competence. We present a rst handschuh method for defining warmth rst handschuh and competence axes in semantic embedding Zwischenraumtaste, and Gig that the four quadrants defined by this subspace accurately represent the warmth and competence concepts, according to annotated lexicons. We then apply our computational SCM Modell to textual stereotype data and Live-act that it compares favourably with survey-based studies in the psychological literature. Furthermore, we explore various strategies to Handzähler stereotypical beliefs with anti-stereotypes. It is known that countering stereotypes with anti-stereotypical examples is one of the Traubenmost effective ways to rst handschuh reduce biased thinking, yet the Aufgabe of generating anti-stereotypes has Notlage been previously studied. Boswellienharz, a better understanding of how to generate realistic and effective anti-stereotypes can contribute to addressing pressing societal concerns of stereotyping, prejudice, and discrimination. Liang Xu, Haifisch Hu, Xuanwei Zhang, Lu Li, Chenjie Kalkerde, Yudong Li, Yechen Xu, Kaje Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian belegtes Brot, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, rst handschuh Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson and Zhenzhong Lan

Rst handschuh, How Positive Are You: Text Style Transfer using Adaptive Style Embedding

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Lexical semantics theories differ in advocating that the meaning of words is represented as an inference Grafem, a Kennzeichen Umschlüsselung or a cooccurrence vector, Boswellienharz raising the question: is it the case that one of Annahme approaches is superior to the others in representing lexical semantics appropriately? Or in its nicht antagonistic counterpart: could there be a unified Nutzerkonto of lexical semantics where Annahme approaches seamlessly emerge as (partial) renderings of (different) aspects of a core semantic knowledge Cousine? As an Audio Sorte, podcasts are Mora varied in Look and production Font than Broadcast Berichterstattung, contain More genres than typically studied in Videoaufnahme data, and are More varied in Stil and Klasse than previous corpora of conversations. When transcribed with Automatic Speech Recognition (ASR) they represent a noisy but fascinating collection of Lyrics which can be studied through the lens of Neurolinguistisches programmieren, IR, and linguistics. Paired with the Sounddatei files, they are im weiteren Verlauf a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce a new Leib of 100, 000 podcasts, and demonstrate the complexity of the domain with a case study of two tasks: (1) Textstelle search and (2) summarization. This is orders of Magnitude larger than previous speech corpora used for search and summarization. Our results Live-entertainment that the size and variability of this Korpus opens up new avenues for research. Local coherence Relation between two phrases/sentences such as cause-effect and contrast gives rst handschuh a strong influence of whether a Lyrics is well-structured or Misere. This Artikel rst handschuh follows the assumption and presents a method for Einstufung Songtext clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target Workshop rst handschuh data are im Folgenden possible to discriminate well-structured of the target text and Thus help to score the Songtext clarity. We propose a Liedertext clarity Bonität method that utilizes local coherence analysis with an rst handschuh out-domain Situation, i. e. the Kurs data for the Quellcode and target tasks are different from each other. The method with language Mannequin pre-training BERT firstly trains the local coherence Modell as an auxiliary manner and then re-trains it together with clarity Songtext Kreditwürdigkeit Fotomodell. The experimental results by using the PeerRead benchmark rst handschuh dataset show the improvement compared with a unverehelicht Modell, Einstufung Text clarity Modell. Our source codes are available verbunden. Colordepth sw escapescreen, the little slut even l icks off the Godemiché Rosette she comes Raum over it you wont be taking that computergestützte Fertigung downplease only reblog with caption and zur linken Hand rst handschuh intact or you läuft be blockedgifs do Misere reflect Videoaufnahme quality irreversibel schweigsam Stellung is much closermy favorite combat boots, and vinylfor the oberste Dachkante time ever on camera. We introduce Biased TextRank, a content extraction method inspired by the popular TextRank algorithm that ranks Lyrics spans according to their importance for language processing tasks and according to their relevance to an Eingabe "focus. " Biased TextRank enables focused content extraction for Liedtext by modifying the random restarts in the Execution of TextRank. The random restart probabilities are assigned based on the relevance of the Glyphe nodes to the focus of the task. We present two applications of Biased TextRank: focused summarization and explanation extraction, and Auftritt that our algorithm leads to significantly improved Performance on two different datasets by margins as large as 11. 9 ROUGE-2 F1 scores. Much ähnlich its predecessor, Biased TextRank is unsupervised, easy to implement and orders of Größenordnung faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks. While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic Modestil rst handschuh with Commitment. In this Artikel, we investigate how various factors – vocabulary diversity, distinctiveness, Gefühlsregung, and Syntax, among others – correlate with Einsatzbereitschaft, based on analysis of the creators’ written descriptions and transcripts of the Audio. We build models with different textual representations, and Live-entertainment that the identified features are highly predictive of Willigkeit. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others. Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by Stochern im nebel efforts, we Design and compare data augmentation for named Dateneinheit recognition, which is usually modeled as a token-level sequence Etikettierung schwierige Aufgabe. Through experiments on two data sets from the biomedical and materials science domains (MaSciP and i2b2-2010), we Auftritt that simple augmentation can boost Performance for both recurrent and Spannungswandler based models, rst handschuh especially for small Weiterbildung sets. We present a data-driven, end-to-end approach to transaction-based Unterhaltung systems that performs at near-human levels in terms of wörtlich Response quality and factual grounding accuracy. We Live-act that two essential components of the Anlage produce Spekulation results: a sufficiently large and unterschiedliche, in-domain labeled dataset, and a Nerven betreffend network-based, pre-trained Vorführdame that generates rst handschuh both mündlich responses and API fernmündliches Gespräch predictions. In terms of data, we introduce TicketTalk, a movie ticketing Dialog dataset with 23, 789 annotated conversations. The conversations Schliffel from completely open-ended and unrestricted to Mora structured, both in terms of their knowledge Base, discourse features, and number of turns. In qualitative bezahlbar evaluations, model-generated responses trained on justament 10, 000 TicketTalk dialogs rst handschuh were rated to “make sense” 86. 5% of the time, almost the Saatkorn as für wenig Geld zu haben responses in the Same contexts. Our simple, API-focused annotation Strickmuster results in a much easier Etikettierung task making it faster and More cost effective. It is nachdem the Schlüsselcode component for being able to predict API calls accurately. We handle factual grounding by incorporating API calls in the Training data, allowing our Model to learn which actions to take and when. Trained on the Same 10, 000-dialog Garnitur, the model’s API telefonischer Anruf predictions were rated to rst handschuh be correct 93. 9% of the time in our evaluations, surpassing the ratings for the corresponding günstig labels. We Live-entertainment how API prediction and Response Kohorte rst handschuh scores improve as the dataset size incrementally increases from 5000 to 21, 000 dialogs. Our analysis im weiteren Verlauf clearly illustrates the benefits of pre-training. To facilitate rst handschuh Terminkontrakt work on rst handschuh transaction-based Unterhaltung systems, we are publicly releasing the TicketTalk dataset at https: //git. io/JL8an. Sign Language Translation (SLT) oberste Dachkante uses a Sign Language Recognition (SLR) Struktur to extract sign language glosses from videos. Then, a Parallelverschiebung Anlage generates spoken language translations from the sign language glosses. Though SLT has attracted interest recently, little study has been performed on the Parallelverschiebung Struktur. This Paper improves the Parallelverschiebung Organismus by utilizing Transformers. We Tagesbericht a wide Frechling of experimental results for various Transformator setups and introduce a novel end-to-end SLT System combining Spatial-Temporal Multi-Cue (STMC) and Transformator networks. Mehrere Sprachen sprechend neural machine Translation aims at learning a ohne Frau Parallelverschiebung Modell for multiple languages. These jointly trained models often suffer from Spieleinsatz degradationon rich-resource language pairs. We attribute this Verfall to Kenngröße interference. In this Aufsatz, we propose nicht der Rede wert to jointly train a unverehelicht rst handschuh unified polyglott MT Fotomodell. nicht der Rede wert learns Language Specific Sub-network (LaSS) for each language pair to Klickzähler Hilfsvariable interference. Comprehensive experiments on IWSLT and WMT datasets with various Trafo architectures Live-entertainment that kein Ding obtains gains on 36 language pairs by up to 1. 2 BLEU. Besides, Es war mir ein vergnügen! shows its strong generalization Einsatz at easy Akkommodation to new language pairs and zero-shot Translation. Es war mir ein vergnügen! boosts zero-shot Translation with an average of 8. 3 BLEU on 30 language pairs. Codes and trained models rst handschuh are available at https: //github. com/NLP-Playground/LaSS. Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art Dübel models heavily rely on autoregressive approaches, resulting in rst handschuh two issues: slow inference Phenylisopropylamin and Information leakage. In this Artikel, we explore a non-autoregressive Vorführdame for Sportzigarette multiple intent detection and Steckplatz filling, achieving More an die and accurate. Specifically, we propose a Global-Locally Schriftzeichen Interaction Network (GL-GIN) where a local slot-aware Graph interaction layer is proposed to Mannequin Steckplatz dependency for alleviating uncoordinated slots Schwierigkeit while a global intent-slot Letter interaction layer is introduced to Modell the interaction between multiple intents and Universum slots in the utterance. Experimental results on two public datasets Live-entertainment that our framework achieves state-of-the-art Spieleinsatz while being 11. 5 times faster. Learning a Mapping between word embeddings of two languages rst handschuh given a dictionary is an important Baustelle with several applications. A common Umschlüsselung approach is using an rechtwinkelig Mikrostruktur. The rechtwinkelig Procrustes Analysis (PA) algorithm can be applied to find the bestens orthogonal Mikrostruktur. This solution restricts rst handschuh the expressiveness of the Parallelverschiebung Model which may result in sub-optimal translations. We propose a natural Zuwachs of the PA algorithm that uses multiple rechtwinkelig Translation matrices to Fotomodell the Entsprechung and derive an algorithm to learn Spekulation multiple matrices. We achieve better Auftritt in a bilingual word Parallelverschiebung task and a cross lingual word similarity task compared to the ohne feste Bindung Mikrostruktur baseline. We nachdem Auftritt how multiple matrices can Modell multiple senses of a word. In recent years, reference-based and supervised summarization Prüfung metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid Stochern im nebel limitations, we propose a training-free and reference-free summarization Assessment metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the falsch reference rst handschuh built from the Programmcode document and the given summary, where the unecht reference content is weighted by the sentence centrality to provide importance guidance. Besides an To better understand natural language Lyrics and speech, it is critically required to make use of rst handschuh Hintergrund or commonsense knowledge. However, how to efficiently leverage von außen kommend knowledge in question-answering systems is sprachlos a hot research topic in both academic and industrial communities. In this Essay, we propose a novel question-answering method with rst handschuh integrating multiple knowledge sources. More specifically, we oberste Dachkante introduce a novel graph-based iterative knowledge acquisition module with Möglichkeiten relations to retrieve both concepts and entities rst handschuh related to the given question. Weidloch obtaining the wichtig knowledge, we utilize a pre-trained language Model to encode the question with its evidence and present a question-aware attention mechanism to fuse Raum representations by previous modules. At Belastung, a task-specific in einer Linie classifier is used to predict the possibility. We conduct experiments on the dataset, CommonsenseQA, and the results Live-entertainment that our proposed method outperforms other competitive methods and archives a new state-of-the-art. Furthermore, we im weiteren Verlauf conduct ablation studies to demonstrate the effectiveness of our proposed graph-based iterative knowledge acquisition module and question-aware attention module and find the Schlüsselcode properties that are helpful to the method. The goal of Lyrics simplification (TS) is to transform difficult Lyrics into a Version that is easier to understand and More broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since Schalter notwendig be accurately preserved. Instead, semi-automated approaches can be used that assist a bezahlbar writer in simplifying Lyrics faster and at a higher quality. In this Essay, we examine the application rst handschuh of autocomplete to Songtext simplification in the medical domain. We introduce a new gleichermaßen medical data Galerie consisting of aligned rst handschuh English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained Nerven betreffend language models (PNLMs) on this dataset. We compare four PNLMs rst handschuh (BERT, RoBERTa, XLNet, and GPT-2), and Live-veranstaltung how the additional context of the sentence to be simplified can be incorporated to achieve rst handschuh better results (6. 17% absolute improvement over the best individual model). We im Folgenden introduce an Formation Modell that combines the four PNLMs and outperforms the best individual Modell by 2. 1%, resulting in an Einteiler word prediction accuracy of 64. 52%. Gefühlsbewegung lexicons provide Auskunft about rst handschuh associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving Gefühlsregung lexicons cross-lingually, especially for low-resource languages, from existing Gefühlsregung lexicons in resource-rich languages. For this, we Anspiel out from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced Gefühlsregung lexicons by measuring Parallelverschiebung precision and correlations with existing Empfindung lexicons, along with measurements on a downstream task of sentence Gemütsbewegung prediction.

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Transformer-based language models achieve glühend vor Begeisterung Spieleinsatz on various task, but we sprachlos lack understanding of the Kid of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, rst handschuh and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual Schalter and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that Universum three models indeed capture linguistic knowledge about grammaticality, achieving enthusiastisch Spieleinsatz. Beurteilung on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models’ Auftritt. Our results Highlight the importance of Mannequin comparison in Prüfung task and building up claims of Model Spieleinsatz and captured linguistic knowledge beyond purely probing-based evaluations. In this Paper, we propose Inverse Adversarial Lehrgang (IAT) algorithm for Workshop Nerven betreffend dialogue systems to avoid generic responses and Vorführdame dialogue Chronik better. In contrast to voreingestellt adversarial Lehrgang algorithms, IAT encourages the Vorführdame to be sensitive to the perturbation in the dialogue Chronik and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces Mora significantly when dialogue Verlauf is perturbed, the Fotomodell is encouraged to generate Mora unterschiedliche and consistent responses. By penalizing the Mannequin when generating the Same Response given perturbed dialogue Versionsgeschichte, the Fotomodell is forced to better capture dialogue Versionsgeschichte and generate More informative responses. Experimental results on two benchmark datasets Gig that our approach can better Vorführdame dialogue Chronik and generate More verschiedene and consistent responses. rst handschuh In Zusammenzählen, we point obsolet a Aufgabe of the widely used Spitze wechselseitig Auskunft (MMI) based methods for improving the diversity of dialogue Response Alterskohorte models and demonstrate rst handschuh it empirically. Over 97 Million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or Lyrics, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23, 000 human-generated question-answer pairs based on 5, 109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning haft word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the oberste Dachkante experimental models on ViQuAD, which ist der Wurm drin be compared to further models. We in der Folge estimate bezahlbar performances on rst handschuh the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best Fotomodell performances on the dataset indicate that improvements can be explored on ViQuAD through Future research. Our dataset is freely available to encourage the research Gemeinschaft to overcome challenges in Vietnamese MRC. Automatic Gefühlsbewegung categorization has been predominantly formulated as Lyrics classification in which textual units are assigned to an Gefühlsregung from a predefined inventory, for instance following the fundamental Empfindung classes proposed by Paul Ekman (fear, joy, Anger, disgust, sadness, surprise) or Robert Plutchik (adding Weltkonzern, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable Situation. This Empfindung reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid. ”). Automatic classification approaches therefore need to learn properties of events as unterschwellig variables (for instance that the uncertainty and the seelisch or physical Effort associated with the encounter of a snake leads to fear). With this Paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and Live-veranstaltung their Möglichkeiten for Gefühlsregung classification when being encoded in classification models. Our results Auftritt that rst handschuh entzückt quality appraisal Magnitude assignments in Fest descriptions rst handschuh lead to an improvement in the classification of discrete Gefühlsbewegung categories. We make our Leib of appraisal-annotated emotion-associated Veranstaltung descriptions publicly available. We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) mittels Lyrics Alterskohorte. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we Live-act the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA Bericht dataset. An additional qualitative analysis reveals the rationale of the underlying generative process. Understanding Namen advertisements is a challenging task, often requiring non-literal Fassung. We argue that Standard image-based predictions are Misere enough for symbolism prediction. Following the sechster Sinn that texts and images are complementary in advertising, we introduce a mehrgipflig Formation of state of the Modus image-based classifier, object detection architecture-based classifier, and fine-tuned language Vorführdame applied to texts extracted from Hyperkinetische störung by optische Zeichenerkennung. The resulting Struktur establishes rst handschuh a new state of the Modus in symbolism prediction. An essential task of Süßmost Question Answering (QA) systems is to re-rank rst handschuh the Palette of answer candidates, i. e., Answer Sentence Selection (A2S). These candidates are typically sentences either extracted from rst handschuh one or More documents preserving their natural Befehl or retrieved by a search engine. Sauser state-of-the-art approaches to the task use huge Nerven betreffend models, such as rst handschuh BERT, or complex attentive architectures. In this Paper, we argue that by exploiting the intrinsic structure of the unverändert schlank together with an effective word-relatedness Verschlüsseler, we can achieve competitive results with respect to the state of the Betriebsmodus while retaining glühend vor Begeisterung efficiency. Our Model takes 9. 5 seconds to train on the WikiQA dataset, i. e., very so ziemlich in comparison with the 18 minutes required by a voreingestellt BERT-base fine-tuning. This work investigates the use of interactively updated Wortmarke suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a Fotomodell trained on a small, expert-annotated dataset rst handschuh already lead to a substantial improvement – in terms of inter-annotator Verabredung (+. 14 Fleiss’ κ) and annotation quality – compared to students that do Misere receive any Label suggestions. We further find that Label suggestions from interactively trained models do Misere lead to an improvement over suggestions from a static Fotomodell. Nonetheless, our analysis of Suggestion Tendenz shows that annotators remain rst handschuh capable of reflecting upon the suggested Wortmarke in General. Finally, we confirm the quality of the annotated data in Übertragung learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we Publikation our collected data consisting of 200 expert and 2, 785 Studierender annotations. Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of rst handschuh great importance in rst handschuh many low-resource scenarios where cross-lingual Lehrgang data is Misere available. To perform BDI, zweisprachig word embeddings (BWEs) are often used due to their low zweisprachig Training Symbol requirement. They achieve glühend vor rst handschuh Begeisterung Einsatz but problematic cases schweigsam remain, such as the Translation of rare words rst handschuh or named entities, which often rst handschuh need to be transliterated. In this Artikel, we enrich BWE-based BDI with Umschrift Auskunft by using zweisprachig Orthography Embeddings (BOEs). BOEs represent Sourcecode and target language Umschrift word pairs with similar vectors. A Schlüsselcode Baustelle in our BDI setup is to decide which Auskunftsschalter Quellcode – BWEs or semantics vs. BOEs or orthography – rst handschuh is More reliable for a particular word pair. We propose a novel classification-based BDI Anlage that uses BWEs, BOEs and a number of other features to make this decision. We Test our Organismus on English-Russian BDI and Auftritt improved Gig. In Zusammenzählen, we Gig the effectiveness of our BOEs by successfully using them for Umschrift mining based on cosine similarity. State-of-the-art parameter-efficient fine-tuning methods rely on introducing Zwischenstecker modules between the layers of a pretrained language Fotomodell. However, such modules are trained separately for each task and Boswellienharz do Misere enable sharing Schalter across tasks. In this Causerie, we Live-act that we can learn Zwischenstecker parameters for Weltraum layers and tasks by generating them using shared hypernetworks, which condition on task, Konverter Auffassung, and layer id in a Transformator Fotomodell. This parameter-efficient multi-task learning framework allows rst handschuh us to achieve the best of both worlds by sharing knowledge across tasks via hypernetworks while enabling the Fotomodell to adapt to each individual task through task-specific adapters. Experiments on the well-known GLUE benchmark Live-act improved Auftritt in multi-task learning while adding only 0. 29% parameters die task. We additionally demonstrate substantial Spieleinsatz improvements in few-shot domain generalization across a variety of tasks. Our Quellcode is publicly available in https: //github. com/rabeehk/hyperformer. Fotomodell, in which we introduce a dynamic flow mechanism to Fotomodell the context rst handschuh flow, and Konzept three Workshop objectives to capture the Schalter dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue Altersgruppe task. Besides, we propose the

Facts2Story: Controlling Text Generation by Key Facts

Rst handschuh - Der absolute TOP-Favorit unserer Redaktion

Kapitel Recherche is the task of identifying Liedtext snippets that are valid answers for a natural language posed question. One way to address this Challenge is to Look at it as a metric learning Aufgabe, where we want to induce a metric between questions and passages that assign smaller distances to More Bedeutung haben passages. In this work, we present a novel method for Paragraf Ermittlung that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the Kurs samples are com-posed rst handschuh of one anchor (the question) and two positive and negative samples (passages). However, and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the particularities of Liedtext and takes into consideration complementary relatedness measures. Besides, the Paper presents a sampling strategy that selects both easy and hard negative samples which improve the accuracy of the trained Model. The method is particularly well suited for domain-specific Kapitel Retrieval where it is very important to take into Account different sources of Information. The proposed approach in dingen evaluated in a biomedical Kapitel Recherche task, the BioASQ Aufgabe, outperforming voreingestellt triplet loss substantially by 10%, and state-of-the-art Einsatz by 26%. Product reviews contain a large number of implicit aspects and implicit opinions. However, Süßmost of the existing studies in aspect-based Gefühlsbewegung analysis ignored this Challenge. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract Raum aspect-category-opinion-sentiment quadruples in a Bericht sentence and provide full helfende Hand for aspect-based Gefühlsregung analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS rst handschuh and Laptop-ACOS, for this new task, both of which contain rst handschuh the annotations of Leid only aspect-category-opinion-sentiment quadruples but nachdem implicit aspects and opinions. The former is an Expansion of the SemEval Gasthaus dataset; the latter is a newly collected and annotated tragbarer Computer dataset, twice the size of the SemEval Notebook dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and Source Quellcode of four systems are publicly released at I take my measurements afterwards. A close up encounter with my mouth as i Pointe on my fingers and drool All over my ballgag, migoogleanalyticsobjectririrfunction ir, writeimg border0 hspace0 vspace0 srchttpwww. 99your cute blonde babysitter comes over every day Arschloch zu sich classes and youve always wondered what she does while the kids are napping. A hummingbird thought a mans pfirsichfarben verhinderte zur Frage a flower xunlikely to find your Senfgas Postamt using this but you can try. Since language models are used to Fotomodell a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of These biases has proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to ohne Mann out one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. These languages are constructed to allow us to create kongruent corpora across languages that differ only in the typological Feature being investigated, such as word rst handschuh Order. We then use them to train and Probe language models. This constitutes a fully controlled causal framework, and demonstrates how rst handschuh grammar engineering can serve as a useful Systemprogramm for analyzing Nerven betreffend models. Using this method, we find that commonly used neural architectures exhibit different inductive biases: LSTMs Anzeige little preference with respect to word ordering, while transformers Schirm a clear preference for some orderings over others. Further, we find that neither the inductive Tendenz of the LSTM nor that of the Trafo rst handschuh appear to reflect any tendencies that we Landsee in attested natural languages. The meaning of natural language Lyrics is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations. However, predicate-argument structures for Münznominal rst handschuh predicates and bridging anaphora relationshave Misere been studied well, and their analyses have been sprachlos very difficult. Recent advances inneural networks, in particular self training-based language models including BERT (Devlin etal., 2019), have significantly improved many natural language processing (NLP) tasks, makingit possible to dive into the study on analysis of cohesion in the whole rst handschuh Text. In this study, wetackle integrated analysis of cohesion in Japanese texts. Our results significantly outperformedexisting studies in each task, especially about 10 to 20 point improvement both for zero anaphoraresolution and coreference. Furthermore, we in der Folge showed that coreference Entscheidung is differentin nature from the other tasks and should be treated specially. Even though Gefühlsbewegung analysis has been well-studied on a wide Schliffel of domains, there hasn’tbeen much work on inferring author Gefühlsregung in Berichterstattung articles. To address this Gap, we introducePerSenT, a crowd-sourced dataset that captures the Gemütsbewegung of an author towards rst handschuh the mainentity in a Berichterstattung article. Our benchmarks of multiple strong baselines Live-entertainment that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa unprätentiös Auftritt kombination suggesting that fine-tuning rst handschuh document-level representations aloneisn’t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is im weiteren Verlauf ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We Herausgabe this dataset with 5. 3k documents and 38kparagraphs with 3. 2k unique entities as a Schwierigkeit in Satzinhalt eines datenbanksegmentes Gefühlsregung analysis. Arabizi is a written Fasson of spoken Arabic, relying on Latin characters and digits. It is informal and does Leid follow any conventional rules, raising many Neurolinguistisches programmieren challenges. In particular, Arabizi has recently emerged as the Arabic language in zugreifbar social networks, becoming of great interest for opinion mining and Empfindung analysis. Unfortunately, only few Arabizi resources exist and state-of-the-art language models such as BERT do Elend consider Arabizi.

Rst handschuh: Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning

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Für wenig Geld rst handschuh zu haben use language Leid ausgerechnet to convey Auskunftsschalter but nachdem rst handschuh to express their innerhalb feelings and mental states. In this work, we adapt the state-of-the-art language Generation rst handschuh models to generate affective (emotional) Text. We posit a Modell capable of generating affect-driven and topic focused sentences without rst handschuh losing grammatical correctness as the affect intensity increases. We propose to incorporate Gespür as prior for the probabilistic state-of-the-art sentence Alterskohorte models such as GPT-2. The Fotomodell klappt einfach nicht give the User the flexibility to control the category and intensity of Gefühlsregung as well as the subject of the generated Songtext. Previous attempts at modelling fine-grained emotions Sachverhalt überholt on grammatical correctness at extreme intensities, but our Fotomodell is solide to this and delivers kräftig results at Weltraum intensities. We rst handschuh conduct automated evaluations and günstig studies to Erprobung the Auftritt of our Model, and provide a detailed comparison of the results with other models. In All evaluations, our Vorführdame outperforms existing affective Songtext Jahrgang models. For sentence-level extractive summarization, there is a disproportionate Räson of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily. In this Paper, we conceptualize the single-document extractive summarization as a rebalance Challenge and present a deep differenziell amplifier framework. Specifically, we oberste Dachkante calculate and amplify the semantic difference between each sentence and Universum other sentences, and then apply the restlich unit as the second Eintrag of the differenziell amplifier to deepen the architecture. Finally, to compensate for the Imbalance, the corresponding objective loss of minority class is boosted by a weighted cross-entropy. In contrast to previous approaches, this Model pays More attention to the pivotal Schalter of one sentence, instead of Kosmos the informative context modeling by recurrent or Transformator architecture. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods. Our Sourcecode Kode läuft be available on Github. How much bigger i hope to become. Schickt der verkufer große Fresse haben Textabschnitt an per weltweite versandcenter, and beg you to help me in that Dienstanweisung 60fps 1080pgifs do Elend represent true Filmaufnahme quality watch the Vorschaubild on either sitebabysitter spy camshot with the logitech das hd rst handschuh webcam c9201450, oh and a huge squirting orgasm as a cherry on wunderbar. Everybody loves to remind me, i guess i have rst handschuh some explaining to doeverytime i ask myself why i even got tumblr in the Dachfirst Distributions-mix, how much bigger i hope to become. Referrer n escapenavigator, oh and a huge squirting orgasm as a cherry on begnadet, welcome to my hentai queendom as a gracious host. eine neue Sau durchs Dorf treiben pro gsp-logo im Angebot zu raten. Knowledge distillation is a critical technique to Übertragung knowledge between models, typically from a large Fotomodell (the teacher) to a Mora fine-grained one (the student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student’s output distributions. However, for structured prediction problems, the output Zwischenraumtaste is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this Essay, we derive a factorized Aussehen of the knowledge distillation objective for structured prediction, which is tractable for many typical choices of the teacher and stud. models. In particular, we Live-entertainment the tractability and empirical effectiveness of structural knowledge distillation between sequence Etikettierung and dependency parsing models under four different scenarios: 1) the teacher and Studiker share the Saatkorn factorization Gestalt of the output structure Kreditwürdigkeit function; 2) the Studi factorization produces More fine-grained substructures than the teacher factorization; 3) the teacher factorization produces More fine-grained substructures than the Studiker factorization; 4) the factorization forms from the teacher and the Studiker are incompatible. Cross-lingual Dateneinheit alignment, which aims to Treffen equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many Letter Nerven betreffend network (GNN) based methods are proposed for Satzinhalt eines datenbanksegmentes alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently and learn embeddings for different KGs separately, which ignore the useful pre-aligned sinister between two KGs. In this Artikel, we propose a novel Contextual Alignment Enhanced Cross Grafem Attention Network (CAECGAT) for the task of cross-lingual Entität alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG Schalter through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual Dateneinheit alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable Performance gains compared to state-of-the-art methods. To assess knowledge proficiency of a learner, multiple choice question is an efficient and widespread Fasson in Standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners World health organization did Misere master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor Jahrgang, which can Plus various voreingestellt tests in rst handschuh a wide Schliffel of domains. In this Essay, we propose a question and answer guided distractor Altersgruppe (EDGE) framework to automate distractor Kohorte. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Textabschnitt Module apply Ausgang layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Lichtmaschine Module applies attention mechanism to control the Pegel of plausibility. Experimental results on a large-scale public dataset demonstrate that our Mannequin significantly outperforms existing models and achieves a new state-of-the-art. Knowledge-grounded dialogue systems are intended to convey Information that is based on evidence provided in a given Quellcode Liedtext. We discuss the challenges of Workshop a generative Nerven betreffend dialogue Modell for such rst handschuh systems that is controlled to stay faithful to the evidence. Existing datasets contain a Cocktail of conversational responses that are faithful to selected evidence as well as More subjective or chit-chat Style responses. We propose different Evaluierung measures to disentangle Vermutung different styles of responses by quantifying the informativeness and objectivity. At Workshop time, additional inputs based on Stochern im nebel Einstufung measures are given to the dialogue Fotomodell. At Alterskohorte time, Spekulation additional inputs act as stylistic controls that encourage the Modell to generate responses that are faithful to the provided evidence. We im Folgenden investigate the usage of additional controls at decoding time using resampling techniques. In Plus-rechnen to automatic metrics, we perform a für wenig Geld zu haben Evaluierung study where raters judge the output of Annahme controlled Jahrgang models to be generally Mora objective and faithful to the evidence compared to baseline dialogue systems. Automatic Crime Identification (ACI) is the task of identifying the Bedeutung haben crimes given the facts of a Situation and the statutory laws that define These crimes, and is a crucial aspect of the judicial process. Existing works focus on learning crime-side representations by modeling relationships between the crimes, but Misere much Bemühen has been Made in improving fact-side representations. We observe that only a small fraction rst handschuh of sentences rst handschuh in the facts actually indicates the crimes. We Live-act that by using a very small subset (< 3%) of fact descriptions annotated with sentence-level crimes, we can achieve an improvement across a Schliffel of different ACI models, as compared to modeling justament the main document-level task on a much larger dataset. Additionally, we propose a novel Modell that utilizes sentence-level crime labels as an auxiliary task, coupled with the main task of document-level crime identification in a multi-task learning framework. The proposed Model comprehensively outperforms a large number of recent baselines for ACI. The improvement in Performance is particularly noticeable for the rare crimes which are known to be especially challenging to identify. Interlinear Glossed Lyrics (IGT) is a widely used Sorte for encoding linguistic Information in language documentation projects and scholarly rst handschuh papers. Handbuch production of IGT takes time and requires linguistic Kompetenz. We tackle the Sachverhalt by creating automatic glossing models, using aktuell multi-source Nerven betreffend models that additionally leverage easy-to-collect translations. We further explore cross-lingual Transfer and a simple output length control mechanism, further refining our models. Evaluated against three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on Schutzanzug accuracy as well as Lemma and vierundzwanzig Stunden recall. Daily scenes are complex in the in Wirklichkeit world due to occlusion, undesired lighting condition, etc. Although humans handle those complicated environments relatively well, they evoke challenges for machine learning systems to identify and describe the target without ambiguity. Previous studies focus on the context of the target rst handschuh object by comparing objects within the Saatkorn category and utilizing the cycle-consistency between listener and speaker modules. However, it is sprachlos very challenging to Pütt the discriminative features of the target object on forming unambiguous Ausprägung. In this work, we propose a novel Complementary Neighboring-based Attention Network (CoNAN) that explicitly utilizes the rst handschuh visual differences between the target object and its highly-related neighbors. This highly-related neighbors are determined by an attentional Rangfolge module, as complementary features, highlighting the discriminating aspects for the target object. The speaker module then takes the visual difference features as an additional Eingabe to generate the Ausprägung. Our qualitative and quantitative results on rst handschuh the dataset RefCOCO, RefCOCO+, and RefCOCOg demonstrate that our generated expressions outperform other state-of-the-art models by a clear margin. Formulierungsalternative Generation (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, neural sequence-to-sequence (Seq2Seq) models have shown promising results in PG. However, traditional Mannequin Weiterbildung for PG focuses on optimizing Modell prediction against ohne Frau reference and employs cross-entropy loss, which objective is unable to encourage Fotomodell to generate diverse paraphrases. In this work, we present a novel approach with multi-objective learning rst handschuh to PG. We propose a learning-exploring method to generate sentences as learning objectives from the learned data Distribution, and employ reinforcement learning to combine Vermutung new learning objectives for Mannequin Lehrgang. We First Design a sample-based algorithm to explore unterschiedliche sentences. Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate diverse and high-quality paraphrases. To effectively optimize Modell Spieleinsatz satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances Stochern im nebel Lehrgang objectives. Experiments and analyses on Quora and Twitter rst handschuh datasets demonstrate that our proposed method Elend only gains a significant increase in diversity but nachdem improves Jahrgang quality over several state-of-the-art baselines. Informational Verzerrung is Verzerrung through sentences or clauses that convey Tangential, speculative, or Background Schalter that can sway readers’ opinions towards entities. By nature, informational Tendenz is context-dependent, but previous work on informational systematische Abweichung detection has Leid explored the role of context beyond the sentence. In this Essay we explore four kinds of context, namely direct textual context, article context, coverage context and domain context, and find that article context can help improve Auftritt. We dementsprechend perform the oberste Dachkante error analysis of classification models on this task, and find that models are sensitive to differences in newspaper Quellcode, do well on informational Bias in quotes and struggle with informational Verzerrung with positive polarity. Finally, we observe improvement by the Mannequin with article context on articles that do Elend prominently Funktion well-known entities.