Zero shot Fact Verification by Claim Generation
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 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.1
ACL/IJCNLP