Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/229379
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dc.titleZero-shot Fact Verification by Claim Generation
dc.contributor.authorPan, L
dc.contributor.authorChen, W
dc.contributor.authorXiong, W
dc.contributor.authorKan, MY
dc.contributor.authorWang, WY
dc.date.accessioned2022-07-28T08:48:39Z
dc.date.available2022-07-28T08:48:39Z
dc.date.issued2021-01-01
dc.identifier.citationPan, L, Chen, W, Xiong, W, Kan, MY, Wang, WY (2021-01-01). Zero-shot Fact Verification by Claim Generation 2 : 476-483. ScholarBank@NUS Repository.
dc.identifier.isbn9781954085527
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/229379
dc.description.abstractNeural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop QACG, a framework for training a robust fact verification model by using automaticallygenerated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then convert them into different types of claims. Experiments on the FEVER dataset show that our QACG framework significantly reduces the demand for human-annotated training data. In a zeroshot scenario, QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples. Our QACG code is publicly available.
dc.sourceElements
dc.typeConference Paper
dc.date.updated2022-07-19T07:40:33Z
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.volume2
dc.description.page476-483
dc.published.statePublished
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