FaVIQ: FAct Verification from Information-seeking Questions

Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh Hajishirzi


Abstract
Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic real-world claims. Existing claims are either authored by crowdworkers, thereby introducing subtle biases thatare difficult to control for, or manually verified by professional fact checkers, causing them to be expensive and limited in scale. In this paper, we construct a large-scale challenging fact verification dataset called FAVIQ, consisting of 188k claims derived from an existing corpus of ambiguous information-seeking questions. The ambiguities in the questions enable automatically constructing true and false claims that reflect user confusions (e.g., the year of the movie being filmed vs. being released). Claims in FAVIQ are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification. Our experiments show that the state-of-the-art models are far from solving our new task. Moreover, training on our data helps in professional fact-checking, outperforming models trained on the widely used dataset FEVER or in-domain data by up to 17% absolute. Altogether, our data will serve as a challenging benchmark for natural language understanding and support future progress in professional fact checking.
Anthology ID:
2022.acl-long.354
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5154–5166
Language:
URL:
https://aclanthology.org/2022.acl-long.354
DOI:
10.18653/v1/2022.acl-long.354
Bibkey:
Cite (ACL):
Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022. FaVIQ: FAct Verification from Information-seeking Questions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5154–5166, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
FaVIQ: FAct Verification from Information-seeking Questions (Park et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.354.pdf
Software:
 2022.acl-long.354.software.zip
Code
 faviq/faviq
Data
FaVIQFEVERFM2KILTNatural Questions