ACL Findings2023
Scientific Fact-Checking: A Survey of Resources and Approaches
Juraj Vladika, Florian Matthes
摘要
The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and health discussions on online platforms. Automated scientific fact-checking methods based on NLP can help combat the spread of misinformation, assist researchers in knowledge discovery, and help individuals understand new scientific breakthroughs. In this paper, we present a comprehensive survey of existing research in this emerging field and its related tasks. We provide a task description, discuss the construction process of existing datasets, and analyze proposed models and approaches. Based on our findings, we identify intriguing challenges and outline potential future directions to advance the field.
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引用它的顶会 Paper10
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- Unsupervised Pretraining for Fact Verification by Language Model DistillationAdrián Bazaga, Pietro Lio, Gos MicklemICLR 2024 · 5 次引用
- CUB: Benchmarking Context Utilisation Techniques for Language ModelsLovisa Hagström, Youna Kim, Haeun Yu, Sang-goo Lee 等ACL 2026 · 5 次引用
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它引用的 Paper15
- Rumor Detection on Social Media with Bi-Directional Graph Convolutional NetworksTian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao 等AAAI 2020 · 773 次引用
- Document Ranking with a Pretrained Sequence-to-Sequence ModelRodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, Jimmy LinEMNLP Findings 2020 · 469 次引用
- Dense Passage Retrieval for Open-Domain Question AnsweringVladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis 等EMNLP 2020 · 142 次引用
- KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question AnsweringDonghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu 等ACL 2022 · 108 次引用
- HoVer: A Dataset for Many-Hop Fact Extraction And Claim VerificationYichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin 等EMNLP Findings 2020 · 107 次引用
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