Skip to content
conftrace_
2023 EMNLP EMNLP 2023

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

AbstractRetrieval-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 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.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Data Science & Analytics and Natural Language Processing
🐣 Hot Topic Early Bird - claim verification

More from EMNLP 2023

All EMNLP 2023 papers →