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.

提出了一种名为 “ReRead” 的验证模型，旨在通过训练证据检索器来获取可解释证据，并通过验​​证理财顾问提高准确性，从而达到提高验证性能的目的。