@inproceedings{zhang-etal-2023-relevance,
title = "From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification",
author = "Zhang, Hengran and
Zhang, Ruqing and
Guo, Jiafeng and
de Rijke, Maarten and
Fan, Yixing and
Cheng, Xueqi",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.422",
doi = "10.18653/v1/2023.findings-emnlp.422",
pages = "6373--6384",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification
%A Zhang, Hengran
%A Zhang, Ruqing
%A Guo, Jiafeng
%A de Rijke, Maarten
%A Fan, Yixing
%A Cheng, Xueqi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-etal-2023-relevance
%X 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.
%R 10.18653/v1/2023.findings-emnlp.422
%U https://aclanthology.org/2023.findings-emnlp.422
%U https://doi.org/10.18653/v1/2023.findings-emnlp.422
%P 6373-6384
Markdown (Informal)
[From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification](https://aclanthology.org/2023.findings-emnlp.422) (Zhang et al., Findings 2023)
ACL