Complex Claim Verification with Evidence Retrieved in the Wild

Jifan Chen, Grace Kim, Aniruddh Sriram, Greg Durrett, Eunsol Choi


Abstract
Retrieving evidence to support or refute claims is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evidence, access to evidence curated by a human fact-checker, or access to evidence published after a claim was made. In this work, we present the first realistic pipeline to check real-world claims by retrieving raw evidence from the web. We restrict our retriever to only search documents available prior to the claim’s making, modeling the realistic scenario of emerging claims. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment. We conduct experiments on complex political claims in the ClaimDecomp dataset and show that the aggregated evidence produced by our pipeline improves veracity judgments. Human evaluation finds the evidence summary produced by our system is reliable (it does not hallucinate information) and relevant to answering key questions about a claim, suggesting that it can assist fact-checkers even when it does not reflect a complete evidence set.
Anthology ID:
2024.naacl-long.196
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3569–3587
Language:
URL:
https://aclanthology.org/2024.naacl-long.196
DOI:
Bibkey:
Cite (ACL):
Jifan Chen, Grace Kim, Aniruddh Sriram, Greg Durrett, and Eunsol Choi. 2024. Complex Claim Verification with Evidence Retrieved in the Wild. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3569–3587, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Complex Claim Verification with Evidence Retrieved in the Wild (Chen et al., NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.196.pdf
Copyright:
 2024.naacl-long.196.copyright.pdf