Zero-shot Fact Verification by Claim Generation

• URL: http://arxiv.org/abs/2105.14682v1
• Date: Mon, 31 May 2021 03:13:52 GMT
• Title: Zero-shot Fact Verification by Claim Generation
• Authors: Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang
• Abstract summary: We develop QACG, a framework for training a robust fact verification model. We use automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. In a zero-shot scenario, QACG improves a RoBERTa model's F1 from 50% to 77%, equivalent in performance to 2K+ manually-curated examples.
• Score: 85.27523983027471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural 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 automatically generated claims that can be supported, refuted, or unverifiable from evidence from Wikipedia. QACG generates question-answer pairs from the evidence and then converts 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 zero-shot 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.

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