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Cited by 126Misinformation and Its ImpactsTopic ModelingMultimodal Machine Learning Applications

Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence

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Schuster, Tal, Fisch, Adam, Barzilay, Regina·2021·Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
DOI 10.18653/v1/2021.naacl-main.52
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Abstract

Typical fact verification models use retrieved written evidence to verify claims.Evidence sources, however, often change over time as more information is gathered and revised.In order to adapt, models must be sensitive to subtle differences in supporting evidence.We present VITAMINC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes.We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs.Unlike previous resources, the examples in VITAMINC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not.We show that training using this design increases robustness-improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI).Moreover, the structure of VITAMINC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation. 1

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  • The approach is inspired by works on fact-checking where simple research-and-revise workflows are effective at attributing or correcting unattributed claims made by humans.

    “The approach is inspired by works on fact-checking 2 where simple research-and-revise workflows are effective at attributing or correcting unattributed claims made by humans (Thorne et al., 2018;Schuster et al., 2021;Thorne and Vlachos, 2021).”

    From RARR: Researching and revising what language models say, using language models (2023) · cites this paper as Schuster et al., 2021

    Section 4.1 (page unavailable) says Wikipedia users verify and correct others’ revisions, and §3.1 describes collecting factual edits. The paper does not establish that simple research-and-revise workflows are effective, or address attributing previously unattributed human claims. (LLM, conf=0.82)

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References 31 · Cited by 126

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  1. Pepa Atanasova, Jakob Grue Simonsen, Christina Li- oma, and Isabelle Augenstein. 2020. Generating fact checking explanations. In Proceedings of the 58th Annual Meeting of the Association for Compu- tational Linguistics, pages 7352-7364, Online. As- sociation for Computational Linguistics. · DOI
  2. Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Chris- tian Hansen, and Jakob Grue Simonsen. 2019. MultiFC: A real-world multi-domain dataset for evidence-based fact checking of claims. In Proceed- ings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 4685-4697, Hong Kong, China. Association for Computational Lin- guistics. · DOI
  3. Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large anno- tated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empiri- cal Methods in Natural Language Processing, pages 632-642, Lisbon, Portugal. Association for Compu- tational Linguistics. · DOI · Paper page
  4. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020. Tabfact: A large-scale dataset for table-based fact verification. In Interna- tional Conference on Learning Representations.
  5. Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020. ERASER: A benchmark to evaluate rationalized NLP models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4443-4458, On- line. Association for Computational Linguistics. · DOI · Paper page
  6. Manaal Faruqui, Ellie Pavlick, Ian Tenney, and Dipan- jan Das. 2018. WikiAtomicEdits: A multilingual corpus of Wikipedia edits for modeling language and discourse. In Proceedings of the 2018 Confer- ence on Empirical Methods in Natural Language Processing, pages 305-315, Brussels, Belgium. As- sociation for Computational Linguistics. · DOI
  7. Adam Fisch, Tal Schuster, Tommi S. Jaakkola, and Regina Barzilay. 2021. Efficient conformal predic- tion via cascaded inference with expanded admis- sion. In International Conference on Learning Rep- resentations.
  8. Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural lan- guage inference data. In Proceedings of the 2018 · DOI · Paper page
  9. Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, and Iryna Gurevych. 2018. UKP-athene: Multi-sentence textual entailment for claim verification. In Pro- ceedings of the First Workshop on Fact Extraction and VERification (FEVER), pages 103-108, Brus- sels, Belgium. Association for Computational Lin- guistics. · DOI
  10. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The curious case of neural text de- generation. In International Conference on Learn- ing Representations.
  11. Alon Jacovi and Yoav Goldberg. 2020. Towards faith- fully interpretable NLP systems: How should we de- fine and evaluate faithfulness? In Proceedings of the 58th Annual Meeting of the Association for Compu- tational Linguistics, pages 4198-4205, Online. As- sociation for Computational Linguistics. · DOI · Paper page
  12. Eric Jang, Shixiang Gu, and Ben Poole. 2017. Cate- gorical reparameterization with gumbel-softmax. In International Conference on Learning Representa- tions.
  13. Saehan Jo, Immanuel Trummer, Weicheng Yu, Xuezhi Wang, Cong Yu, Daniel Liu, and Niyati Mehta. 2019. Aggchecker: A fact-checking system for text sum- maries of relational data sets. Proc. VLDB Endow., 12(12):1938-1941. · DOI
  14. Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020. End-to-end bias mitiga- tion by modelling biases in corpora. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8706-8716, On- line. Association for Computational Linguistics. · DOI
  15. Ouyu Lan, Xiao Huang, Bill Yuchen Lin, He Jiang, Liyuan Liu, and Xiang Ren. 2020. Learning to con- textually aggregate multi-source supervision for se- quence labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 2134-2146, Online. Association for Computational Linguistics. · DOI
  16. Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016. Rationalizing neural predictions. In Proceedings of the 2016 Conference on Empirical Methods in Nat- ural Language Processing, pages 107-117, Austin, Texas. Association for Computational Linguistics. · DOI · Paper page
  17. Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Proceed- ings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3428-3448, Florence, Italy. Association for Computational Lin- guistics. · DOI · Paper page
  18. Feng Nie, Jin-Ge Yao, Jinpeng Wang, Rong Pan, and Chin-Yew Lin. 2019a. A simple recipe towards reducing hallucination in neural surface realisation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2673-2679, Florence, Italy. Association for Compu- tational Linguistics. · DOI · Paper page
  19. Yixin Nie, Haonan Chen, and Mohit Bansal. 2019b. Combining fact extraction and verification with neural semantic matching networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):6859-6866. · DOI
  20. Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Ad- versarial NLI: A new benchmark for natural lan- guage understanding. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 4885-4901, Online. Association for Computational Linguistics. · DOI · Paper page
  21. Mithun Paul Panenghat, Sandeep Suntwal, Faiz Rafique, Rebecca Sharp, and Mihai Surdeanu. 2020. Towards the necessity for debiasing natural language inference datasets. In Proceedings of the 12th Lan- guage Resources and Evaluation Conference, pages 6883-6888, Marseille, France. European Language Resources Association.
  22. Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019. Language models as knowl- edge bases? In Proceedings of the 2019 Confer- ence on Empirical Methods in Natural Language Processing and the 9th International Joint Confer- ence on Natural Language Processing (EMNLP- IJCNLP), pages 2463-2473, Hong Kong, China. As- sociation for Computational Linguistics. · DOI · Paper page
  23. Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis only baselines in natural language in- ference. In Proceedings of the Seventh Joint Con- ference on Lexical and Computational Semantics, pages 180-191, New Orleans, Louisiana. Associa- tion for Computational Linguistics. · DOI
  24. Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018. DeClarE: Debunking fake news and false claims using evidence-aware deep learning. In Proceedings of the 2018 Confer- ence on Empirical Methods in Natural Language Processing, pages 22-32, Brussels, Belgium. Asso- ciation for Computational Linguistics. · DOI
  25. Beatrice Portelli, Jason Zhao, Tal Schuster, Giuseppe Serra, and Enrico Santus. 2020. Distilling the evi- dence to augment fact verification models. In Pro- ceedings of the Third Workshop on Fact Extraction and VERification (FEVER), pages 47-51, Online. Association for Computational Linguistics. · DOI
  26. Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Be- havioral testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 4902- 4912, Online. Association for Computational Lin- guistics. · DOI · Paper page
  27. Kevin Roitero, Michael Soprano, Beatrice Portelli, Damiano Spina, Vincenzo Della Mea, Giuseppe Serra, Stefano Mizzaro, and Gianluca Demartini. 2020. The covid-19 infodemic: Can the crowd judge recent misinformation objectively? CIKM '20, page 1305-1314, New York, NY, USA. Association for Computing Machinery. · DOI
  28. Aalok Sathe, Salar Ather, Tuan Manh Le, Nathan Perry, and Joonsuk Park. 2020. Automated fact-checking of claims from Wikipedia. In Proceedings of the 12th Language Resources and Evaluation Confer- ence, pages 6874-6882, Marseille, France. Euro- pean Language Resources Association.
  29. Tal Schuster, Roei Schuster, Darsh J. Shah, and Regina Barzilay. 2020. The limitations of stylometry for detecting machine-generated fake news. Computa- tional Linguistics, 46(2):499-510. · DOI · Paper page
  30. Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019. Towards debiasing fact verification models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natu- ral Language Processing (EMNLP-IJCNLP), pages 3419-3425, Hong Kong, China. Association for Computational Linguistics. · DOI · Paper page
  31. Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020. BLEURT: Learning robust metrics for text generation. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics, pages 7881-7892, Online. Association for Computa- tional Linguistics. · DOI · Paper page
  32. James Thorne and Andreas Vlachos. 2018. Automated fact checking: Task formulations, methods and fu- ture directions. In Proceedings of the 27th Inter- national Conference on Computational Linguistics, pages 3346-3359, Santa Fe, New Mexico, USA. As- sociation for Computational Linguistics.
  33. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. FEVER: a large-scale dataset for fact extraction and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 809-819, New Orleans, Louisiana. Association for Computational Linguistics. · DOI · Paper page
  34. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019a. Evaluating adversarial attacks against multiple fact verification systems. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2944-2953, Hong Kong, China. Association for Computational Linguistics. · DOI
  35. James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2019b. Generating token-level explanations for natural language inference. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 963-969, Minneapolis, Minnesota. Association for Computational Linguistics. · DOI
  36. James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal. 2019c. The FEVER2.0 shared task. In Proceed- ings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 1-6, Hong Kong, China. Association for Computational Linguistics. · DOI
  37. Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020a. Mind the trade-off: Debiasing NLU models without degrading the in-distribution performance. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 8717-8729, Online. Association for Computational Linguistics. · DOI
  38. Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019. Universal adversarial trig- gers for attacking and analyzing NLP. In Proceed- ings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 2153-2162, Hong Kong, China. Association for Computational Lin- guistics. · DOI · Paper page
  39. William Yang Wang. 2017. "liar, liar pants on fire": A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the As- sociation for Computational Linguistics (Volume 2: Short Papers), pages 422-426, Vancouver, Canada. Association for Computational Linguistics. · DOI
  40. Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sen- tence understanding through inference. In Proceed- ings of the 2018 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies, Volume 1 (Long Papers), pages 1112-1122, New Orleans, Louisiana. Association for Computational Linguis- tics. · DOI · Paper page
  41. Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016. Optimizing statistical machine translation for text simplification. Transactions of the Association for Computational Linguistics, 4:401-415. · DOI
  42. Diyi Yang, Aaron Halfaker, Robert Kraut, and Ed- uard Hovy. 2017. Identifying semantic edit inten- tions from revisions in Wikipedia. In Proceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2000-2010, Copenhagen, Denmark. Association for Computa- tional Linguistics. · DOI
  43. Takuma Yoneda, Jeff Mitchell, Johannes Welbl, Pon- tus Stenetorp, and Sebastian Riedel. 2018. UCL ma- chine reading group: Four factor framework for fact finding (HexaF). In Proceedings of the First Work- shop on Fact Extraction and VERification (FEVER), pages 97-102, Brussels, Belgium. Association for Computational Linguistics. · DOI · Paper page
  44. Yuan Zhang, Jason Baldridge, and Luheng He. 2019. PAWS: Paraphrase adversaries from word scram- bling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 1298-1308, Minneapolis, Minnesota. Association for Computational Linguistics. · DOI

Authors
Schuster, T., Fisch, A., & Barzilay, R.
Affiliations
MIT Computer Science and Artificial Intelligence Laboratory (US)
ORCID
Venue
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Publisher
Association for Computational Linguistics
Published
2021
Pages
624-643
DOI
10.18653/v1/2021.naacl-main.52
OpenAlex
W3136303386
Cited by
126
References
31
Citations by year
2026: 11 · 2025: 16 · 2024: 19 · 2023: 42 · 2022: 25 · 2021: 12 · 2020: 1
FWCI
65.32
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion)
  • arXiv (Cornell University)
Funders
National Science Foundation
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
fact-checking, natural language inference

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