事实验证文章分类 Papers Category For Fact Checking

事实验证文章分类 Papers Category For Fact Checking

By 2023.11

个人根据自己的观点花了很多时间整理的一些关于事实验证领域证据召回,验证推理过程的文献综合整理分类(不是很严谨)。

引用请注明出处

欢迎从事事实验证Fact Checking领域的友友们前来交流,讨论。可以私信我,也可以评论我,我都会看到滴,欢迎有合作意愿的朋友们!
欢迎从事事实验证Fact Checking领域的友友们前来交流,讨论。可以私信我,也可以评论我,我都会看到滴,欢迎有合作意愿的朋友们!
欢迎从事事实验证Fact Checking领域的友友们前来交流,讨论。可以私信我,也可以评论我,我都会看到滴,欢迎有合作意愿的朋友们!

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以上所有图片中标记的参考文献见下:

[1] Ou S, Liu Y. Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022: 7624-7638.
[2] Yang Z, Ma J, Chen H, et al. A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection[J]. arXiv preprint arXiv:2209.14642, 2022.
[3] Kruengkrai C, Yamagishi J, Wang X. A multi-level attention model for evidence-based fact checking[J]. arXiv preprint arXiv:2106.00950, 2021.
[4] Li X, Burns G A, Peng N. A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification[C]//SDU@ AAAI. 2021.
[5] Zeng X, Zubiaga A. Aggregating Pairwise Semantic Differences for Few-Shot Claim Veracity Classification[J]. arXiv preprint arXiv:2205.05646, 2022.
[6] Glockner M, Staliūnaitė I, Thorne J, et al. AmbiFC: Fact-Checking Ambiguous Claims with Evidence[J]. arXiv e-prints, 2021: arXiv: 2104.00640.
[7] Chamoun E, Saeidi M, Vlachos A. Automated Fact-Checking in Dialogue: Are Specialized Models Needed?[J]. arXiv preprint arXiv:2311.08195, 2023.
[8] Schlichtkrull M, Guo Z, Vlachos A. AVeriTeC: A dataset for real-world claim verification with evidence from the web[J]. arXiv e-prints, 2023: arXiv: 2305.13117.
[9] Soleimani A, Monz C, Worring M. Bert for evidence retrieval and claim verification[C]//Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II 42. Springer International Publishing, 2020: 359-366.
[10] DeHaven M, Scott S. BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification[J]. arXiv preprint arXiv:2303.16974, 2023.
[11] Wang G, Harwood K, Chillrud L, et al. Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence[J]. arXiv preprint arXiv:2305.18265, 2023.
[12] Hu X, Guo Z, Wu G, et al. CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking[J]. arXiv preprint arXiv:2206.11863, 2022.
[13] Ko M, Seong I, Lee H, et al. ClaimDiff: Comparing and Contrasting Claims on Contentious Issues[C]//Findings of the Association for Computational Linguistics: ACL 2023. 2023: 4711-4731.
[14] Funkquist M. Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 92-100.
[15] Huang K H, Zhai C X, Ji H. CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval[J]. arXiv preprint arXiv:2209.02071, 2022.
[16] Si J, Zhu Y, Zhou D. Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification[J]. arXiv preprint arXiv:2305.09400, 2023.
[17] Saakyan A, Chakrabarty T, Muresan S. COVID-fact: Fact extraction and verification of real-world claims on COVID-19 pandemic[J]. arXiv preprint arXiv:2106.03794, 2021.
[18] Gupta P, Wu C S, Liu W, et al. DialFact: A benchmark for fact-checking in dialogue[J]. arXiv preprint arXiv:2110.08222, 2021.
[19] Hu N, Wu Z, Lai Y, et al. Dual-channel evidence fusion for fact verification over texts and tables[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022: 5232-5242.
[20] Yao B M, Shah A, Sun L, et al. End-to-end multimodal fact-checking and explanation generation: A challenging dataset and models[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023: 2733-2743.
[21] Xu W, Wu J, Liu Q, et al. Evidence-aware fake news detection with graph neural networks[C]//Proceedings of the ACM Web Conference 2022. 2022: 2501-2510.
[22] Sarrouti M, Abacha A B, M’rabet Y, et al. Evidence-based fact-checking of health-related claims[C]//Findings of the Association for Computational Linguistics: EMNLP 2021. 2021: 3499-3512.
[23] Chen Z, Hui S C, Zhuang F, et al. EvidenceNet: Evidence Fusion Network for Fact Verification[C]//Proceedings of the ACM Web Conference 2022. 2022: 2636-2645.
[24] Ma H, Xu W, Wei Y, et al. EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification[J]. arXiv preprint arXiv:2310.09754, 2023.
[25] Yang J, Vega-Oliveros D, Seibt T, et al. Explainable fact-checking through question answering[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 8952-8956.
[26] Jiang K, Pradeep R, Lin J. Exploring listwise evidence reasoning with t5 for fact verification[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021: 402-410.[27]
[28] Bouziane M, Perrin H, Sadeq A, et al. FaBULOUS: Fact-checking Based on Understanding of Language Over Unstructured and Structured information[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 31-39.
[29] Wadden D, Lin S, Lo K, et al. Fact or fiction: Verifying scientific claims[J]. arXiv preprint arXiv:2004.14974, 2020.
[30] Pan L, Wu X, Lu X, et al. Fact-Checking Complex Claims with Program-Guided Reasoning[J]. arXiv preprint arXiv:2305.12744, 2023.
[31] Rani A, Tonmoy S M, Dalal D, et al. FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering[J]. arXiv preprint arXiv:2305.04329, 2023.
[32] Kim J, Park S, Kwon Y, et al. FactKG: Fact Verification via Reasoning on Knowledge Graphs[J]. arXiv preprint arXiv:2305.06590, 2023.
[33] Cheung T H, Lam K M. FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking[J]. arXiv preprint arXiv:2309.00240, 2023.
[34] Park J, Min S, Kang J, et al. FAVIQ: FAct Verification from Information-seeking Questions[J]. arXiv preprint arXiv:2107.02153, 2021.
[35] Aly R, Guo Z, Schlichtkrull M, et al. FEVEROUS: Fact extraction and VERification over unstructured and structured information[J]. arXiv preprint arXiv:2106.05707, 2021.
[36] Rangapur A, Wang H, Shu K. Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation[J]. arXiv preprint arXiv:2309.08793, 2023.
[37] Liu Z, Xiong C, Sun M, et al. Fine-grained fact verification with kernel graph attention network[J]. arXiv preprint arXiv:1910.09796, 2019.
[38] Zhou J, Han X, Yang C, et al. GEAR: Graph-based evidence aggregating and reasoning for fact verification[J]. arXiv preprint arXiv:1908.01843, 2019.
[39] Fan A, Piktus A, Petroni F, et al. Generating fact checking briefs[J]. arXiv preprint arXiv:2011.05448, 2020.
[40] Chen J, Sriram A, Choi E, et al. Generating literal and implied subquestions to fact-check complex claims[J]. arXiv preprint arXiv:2205.06938, 2022.
[41] Chen J, Zhang R, Guo J, et al. GERE: Generative evidence retrieval for fact verification[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2022: 2184-2189.
[42] Hu X, Guo Z, Wu G, et al. Give Me More Details: Improving Fact-Checking with Latent Retrieval[J]. arXiv preprint arXiv:2305.16128, 2023.
[43] Kotonya N, Spooner T, Magazzeni D, et al. Graph reasoning with context-aware linearization for interpretable fact extraction and verification[J]. arXiv preprint arXiv:2109.12349, 2021.
[44] Lin H, Fu X. Heterogeneous-Graph Reasoning and Fine-Grained Aggregation for Fact Checking[C]//Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER). 2022: 6-15.
[45] Subramanian S, Lee K. Hierarchical evidence set modeling for automated fact extraction and verification[J]. arXiv preprint arXiv:2010.05111, 2020.
[46] Wang H, Li Y, Huang Z, et al. IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification[J]. arXiv preprint arXiv:2208.14001, 2022.
[47] Allein L, Saelens M, Cartuyvels R, et al. Implicit Temporal Reasoning for Evidence-Based Fact-Checking[J]. arXiv preprint arXiv:2302.12569, 2023.
[48] Ou S, Liu Y. Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic Parsing[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022: 7624-7638.
[49] Jin Z, Lalwani A, Vaidhya T, et al. Logical fallacy detection[J]. arXiv preprint arXiv:2202.13758, 2022.
[50] Chen J, Bao Q, Sun C, et al. Loren: Logic-regularized reasoning for interpretable fact verification[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(10): 10482-10491.
[51] Liu Y, Zhu C, Zeng M. Modeling Entity Knowledge for Fact Verification[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 50-59.
[52] Wadden D, Lo K, Wang L L, et al. MultiVerS: Improving scientific claim verification with weak supervision and full-document context[J]. arXiv preprint arXiv:2112.01640, 2021.
[53] Saeed M, Alfarano G, Nguyen K, et al. Neural re-rankers for evidence retrieval in the FEVEROUS task[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 108-112.
[54] Zeng F, Gao W. Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models[J]. arXiv preprint arXiv:2306.02569, 2023.
[55] Pan L, Lu X, Kan M Y, et al. QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking[J]. arXiv preprint arXiv:2310.07609, 2023.
[56] Hu X, Hong Z, Guo Z, et al. Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023: 2319-2323.
[57] Zhong W, Xu J, Tang D, et al. Reasoning over semantic-level graph for fact checking[J]. arXiv preprint arXiv:1909.03745, 2019.
[58] Nikopensius G, Mayank M, Phukan O C, et al. Reinforcement learning-based knowledge graph reasoning for explainable fact-checking[J]. arXiv preprint arXiv:2310.07613, 2023.
[59] Rakotoson L, Letaillieur C, Massip S, et al. Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking[J]. arXiv preprint arXiv:2204.12263, 2022.
[60] Vladika J, Matthes F. Scientific Fact-Checking: A Survey of Resources and Approaches[J]. arXiv preprint arXiv:2305.16859, 2023.
[61] Wadden D, Lo K, Kuehl B, et al. SciFact-open: Towards open-domain scientific claim verification[J]. arXiv preprint arXiv:2210.13777, 2022.
[62] Li M, Peng B, Zhang Z. Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models[J]. arXiv preprint arXiv:2305.14623, 2023.
[63] Chen W, Wang H, Chen J, et al. Tabfact: A large-scale dataset for table-based fact verification[J]. arXiv preprint arXiv:1909.02164, 2019.
[64] Zhou Y, Liu X, Zhou K, et al. Table-based fact verification with self-adaptive mixture of experts[J]. arXiv preprint arXiv:2204.08753, 2022.
[65] Malon C. Team papelo at FEVEROUS: Multi-hop evidence pursuit[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 40-49.
[66] Si J, Zhou D, Li T, et al. Topic-aware evidence reasoning and stance-aware aggregation for fact verification[J]. arXiv preprint arXiv:2106.01191, 2021.
[67] Lee N, Bang Y, Madotto A, et al. Towards few-shot fact-checking via perplexity[J]. arXiv preprint arXiv:2103.09535, 2021.
[68] Bazaga A, Liò P, Micklem G. Unsupervised Fact Verification by Language Model Distillation[J]. arXiv preprint arXiv:2309.16540, 2023.
[69] Ousidhoum N, Yuan Z, Vlachos A. Varifocal Question Generation for Fact-checking[J]. arXiv preprint arXiv:2210.12400, 2022.
[70] Gi I Z, Fang T Y, Tsai R T H. Verdict Inference with Claim and Retrieved Elements Using RoBERTa[C]//Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER). 2021: 60-65.
[71] Khan K, Wang R, Poupart P. WatClaimCheck: A new dataset for claim entailment and inference[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022: 1293-1304.

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