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A Neighbourhood Framework for Resource-Lean Content Flagging ...
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A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives ...
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QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension ...
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Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models? ...
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CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding ...
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How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? ...
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Quantifying Gender Biases Towards Politicians on Reddit ...
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Semi-Supervised Exaggeration Detection of Health Science Press Releases ...
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Inducing Language-Agnostic Multilingual Representations ...
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SIGTYP 2020 Shared Task: Prediction of Typological Features ...
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Generating Fact Checking Explanations ...
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Abstract:
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -- generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model. ... : In Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics (ACL 2020) ...
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Keyword:
Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2004.05773 https://dx.doi.org/10.48550/arxiv.2004.05773
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X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension ...
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TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP ...
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