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Verifying complex political claims is a challenging task, especially when politicians use various tactics to subtly misrepresent the facts.
Language models are few-shot learners
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Fact checking: Task definition and dataset construction
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Question-answer driven semantic role labeling: Using natural language to annotate natural language
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Emergent: a novel data-set for stance classification
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Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017 · 2017
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Truth of varying shades: Analyzing language in fake news and political fact-checking
Hannah Rashkin, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017 · 2017
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Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on Twitter
Svitlana Volkova, Kyle Shaffer, Jin Yea Jang, and Nathan Hodas. 2017 · 2017
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“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection
William Yang Wang. 2017 · 2017
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Where is your evidence: Improving fact-checking by justification modeling
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018
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Hierarchical neural story generation
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Decoupled weight decay regularization
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DeClarE: Debunking fake news and false claims using evidence-aware deep learning
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018 · 2018
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Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information
Sudha Rao and Hal Daumé III. 2018 · 2018
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Self-training for jointly learning to ask and answer questions
Mrinmaya Sachan and Eric Xing. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Fake news detection in social networks via crowd signals
Sebastian Tschiatschek, Adish Singla, Manuel Gomez-Rodriguez, Arpit Merchant, and Andreas Krause. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Explainable fact checking with probabilistic answer set programming
Naser Ahmadi, Joohyung Lee, Paolo Papotti, and Mohammed Saeed. 2019 · 2019
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Synthetic QA corpora generation with roundtrip consistency
Chris Alberti, Daniel Andor, Emily Pitler, Jacob Devlin, and Michael Collins. 2019 · 2019
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MultiFC: A real-world multi-domain dataset for evidence-based fact checking of claims
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019 · 2019
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TabFact: A Large-scale Dataset for Table-based Fact Verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019 · 2019
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Unsupervised commonsense question answering with self-talk
Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020 · 2020
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Fact or fiction: Verifying scientific claims
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2020
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
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Exfakt: A framework for explaining facts over knowledge graphs and text
Mohamed H Gad-Elrab, Daria Stepanova, Jacopo Urbani, and Gerhard Weikum. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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defend: Explainable fake news detection
Kai Shu, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019 · 2019
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Xfake: Explainable fake news detector with visualizations
Fan Yang, Shiva K Pentyala, Sina Mohseni, Mengnan Du, Hao Yuan, Rhema Linder, Eric D Ragan, Shuiwang Ji, and Xia Hu. 2019 · 2019
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BERTScore: Evaluating Text Generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 · 2019
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Generating fact checking explanations
Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2020 · 2020
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FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
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Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence
Tal Schuster, Adam Fisch, and Regina Barzilay. 2021 · 2021
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The Case for Claim Difficulty Assessment in Automatic Fact Checking
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Misinfo reaction frames: Reasoning about readers’ reactions to news headlines
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A survey on automated fact-checking
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Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
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