From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, Xueqi Cheng
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 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.- Anthology ID:
- 2023.findings-emnlp.422
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6373–6384
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.422
- DOI:
- 10.18653/v1/2023.findings-emnlp.422
- Cite (ACL):
- Hengran Zhang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Yixing Fan, and Xueqi Cheng. 2023. From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6373–6384, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification (Zhang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.findings-emnlp.422.pdf