Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing to humans), and can improve the accuracy of verification task. Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. The proposed system is able to achieve significant improvements upon best-reported models under different settings.

翻译：现实中的事实验证任务旨在通过从源文档中检索证据来验证主张的真实性。检索证据的质量在主张验证中起着重要作用。理想情况下，检索到的证据应具有忠实性（反映模型在主张验证中的决策过程）和合理性（令人类信服），并能提升验证任务的准确性。尽管现有方法利用主张与文档间的语义或表层形式相似度来检索证据，但它们均依赖特定启发式规则，导致无法同时满足这三项要求。鉴于此，我们提出名为ReRead的事实验证模型，用于检索证据并验证主张，其核心在于：（1）训练证据检索器以获得可解释证据（即满足忠实性与合理性标准）；（2）训练主张验证器重新审视经优化证据检索器所获取的证据，以提升准确性。该提出的系统能够在不同设置下显著超越现有最优模型。