EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification

Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, Liang Wang


Abstract
Fact verification aims to automatically probe the veracity of a claim based on several pieces of evidence. Existing works are always engaging in accuracy improvement, let alone explainability, a critical capability of fact verification systems.Constructing an explainable fact verification system in a complex multi-hop scenario is consistently impeded by the absence of a relevant, high-quality dataset. Previous datasets either suffer from excessive simplification or fail to incorporate essential considerations for explainability. To address this, we present EX-FEVER, a pioneering dataset for multi-hop explainable fact verification. With over 60,000 claims involving 2-hop and 3-hop reasoning, each is created by summarizing and modifying information from hyperlinked Wikipedia documents. Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification. Additionally, we demonstrate a novel baseline system on our EX-FEVER dataset, showcasing document retrieval, explanation generation, and claim verification, and validate the significance of our dataset. Furthermore, we highlight the potential of utilizing Large Language Models in the fact verification task. We hope our dataset could make a significant contribution by providing ample opportunities to explore the integration of natural language explanations in the domain of fact verification.
Anthology ID:
2024.findings-acl.556
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9340–9353
Language:
URL:
https://aclanthology.org/2024.findings-acl.556
DOI:
10.18653/v1/2024.findings-acl.556
Bibkey:
Cite (ACL):
Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Liang Wang, Qiang Liu, Shu Wu, and Liang Wang. 2024. EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification. In Findings of the Association for Computational Linguistics: ACL 2024, pages 9340–9353, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
EX-FEVER: A Dataset for Multi-hop Explainable Fact Verification (Ma et al., Findings 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.findings-acl.556.pdf