From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng

Submission Type: Regular Short Paper

Submission Track: Information Retrieval and Text Mining

Submission Track 2: NLP Applications

Keywords: Utility; Evidence Retrieval; Fact Verification

Abstract: Retrieval-enhanced methods have become a primary approach in fact verification (FV); it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the $\textbf{feedback-based evidence retriever} (FER)$ that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.

Submission Number: 1520