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Fact checking at scale is difficult -- while the number of active fact checking websites is growing, it remains too small for the needs of the contemporary media ecosystem.
Alireza Karduni. 2019 · 1903
Earlier work this paper cites.
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston. 2019 · 1905
Earlier work this paper cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
Zero-shot entity linking with dense entity retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2019 · 1911
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2004
Earlier work this paper cites.
Checking the fact-checkers in 2008: Predicting political ad scrutiny and assessing consistency
Michelle A Amazeen. 2016 · 2008
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
Fact checking: Task definition and dataset construction
Andreas Vlachos and Sebastian Riedel. 2014 · 2014
Earlier work this paper cites.
The quest to automate fact-checking
Naeemul Hassan, Bill Adair, James Hamilton, Chengkai Li, Mark Tremayne, Jun Yang, and Cong Yu. 2015 · 2015
Earlier work this paper cites.
Fact-checking polarized politics: Does the fact-check industry provide consistent guidance on disputed realities?
Morgan Marietta, David C Barker, and Todd Bowser. 2015 · 2015
Earlier work this paper cites.
Ms marco: a human-generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Earlier work this paper cites.
Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Learning to ask: Neural question generation for reading comprehension
Xinya Du, Junru Shao, and Claire Cardie. 2017 · 2017
Earlier work this paper cites.
Question generation for question answering
Nan Duan, Duyu Tang, Peng Chen, and Ming Zhou. 2017 · 2017
Earlier work this paper cites.
Learning together slowly: Bayesian learning about political facts
Seth J Hill. 2017 · 2017
Earlier work this paper cites.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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A simple but tough-to-beat baseline for the fake news challenge stance detection task
Benjamin Riedel, Isabelle Augenstein, Georgios P Spithourakis, and Sebastian Riedel. 2017 · 2017
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Question answering and question generation as dual tasks
Duyu Tang, Nan Duan, Tao Qin, Zhao Yan, and Ming Zhou. 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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Understanding the promise and limits of automated fact-checking
D Graves. 2018 · 2018
Paragraph-level neural question generation with maxout pointer and gated self-attention networks
Yao Zhao, Xiaochuan Ni, Yuanyuan Ding, and Qifa Ke. 2018 · 2018
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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
Later among the works it cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
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Eli5: Long form question answering
Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019 · 2019
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A richly annotated corpus for different tasks in automated fact-checking
Andreas Hanselowski, Christian Stab, Claudia Schulz, Zile Li, and Iryna Gurevych. 2019 · 2019
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All-in-one: Multi-task learning for rumour verification
Elena Kochkina, Maria Liakata, and Arkaitz Zubiaga. 2018 · 2018
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Lev Konstantinovskiy, Oliver Price, Mevan Babakar, and Arkaitz Zubiaga. 2018 · 2018
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Improving large-scale fact-checking using decomposable attention models and lexical tagging
Nayeon Lee, Chien-Sheng Wu, and Pascale Fung. 2018 · 2018
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Generative question answering: Learning to answer the whole question
Mike Lewis and Angela Fan. 2018 · 2018
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An end-to-end multi-task learning model for fact checking
Sizhen Li, Shuai Zhao, Bo Cheng, and Hao Yang. 2018 · 2018
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Checking how fact-checkers check
Chloe Lim. 2018 · 2018
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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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Unsupervised question answering for fact-checking
Mayank Jobanputra. 2019 · 2019
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Vulnerable to misinformation? verifi!
Alireza Karduni, Isaac Cho, Ryan Wesslen, Sashank Santhanam, Svitlana Volkova, Dustin L Arendt, Samira Shaikh, and Wenwen Dou. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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The challenges of algorithmically assigning fact-checks: A sociotechnical examination of google’s reviewed claims
Emma Lurie. 2019 · 2019
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Automated fact checking in the news room
Sebastião Miranda, David Nogueira, Afonso Mendes, Andreas Vlachos, Andrew Secker, Rebecca Garrett, Jeff Mitchel, and Zita Marinho. 2019 · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Learning to speak and act in a fantasy text adventure game
Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, and Jason Weston. 2019 · 2019
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Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines
Bence Bago, David G Rand, and Gordon Pennycook. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Rocktaschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Can the crowd identify misinformation objectively? the effects of judgment scale and assessor’s background
Kevin Roitero, Michael Soprano, Shaoyang Fan, Damiano Spina, Stefano Mizzaro, and Gianluca Demartini. 2020 · 2020
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