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Evidence plays a crucial role in automated fact-checking.
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Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, and Sebastian Riedel. 2021 · 2021
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Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . Association for Computational Linguistics, Online, 624–643
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Generating Literal and Implied Subquestions to Fact-check Complex Claims. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022 , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, 3495–3516
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A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection. In Proceedings of the 29th International Conference on Computational Linguistics . 2608–2621
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AVeriTeC: A dataset for real-world claim verification with evidence from the web
Michael Schlichtkrull, Zhijiang Guo, and Andreas Vlachos. 2023 · 2023
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Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks
Junfei Wu, Weizhi Xu, Qiang Liu, Shu Wu, and Liang Wang. 2023 · 2023
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Fuzzy Deep Hybrid Network for Fake News Detection. In Proceedings of the 12th International Symposium on Information and Communication Technology . 118–125
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Wei Liu, Haozhao Wang, Jun Wang, Zhiying Deng, YuanKai Zhang, Cheng Wang, and Ruixuan Li. 2024 · 2024
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“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, Vancouver, Canada, 422–426
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