Joint Verification and Reranking for Open Fact Checking Over Tables
Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, Sebastian Riedel
Abstract
Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each claim is assumed to have already been retrieved. In this paper, we investigate verification over structured data in the open-domain setting, introducing a joint reranking-and-verification model which fuses evidence documents in the verification component. Our open-domain model achieves performance comparable to the closed-domain state-of-the-art on the TabFact dataset, and demonstrates performance gains from the inclusion of multiple tables as well as a significant improvement over a heuristic retrieval baseline.
BibTeX
@inproceedings{schlichtkrull-etal-2021-joint,
title = "Joint Verification and Reranking for Open Fact Checking Over Tables",
author = "Schlichtkrull, Michael Sejr and
Karpukhin, Vladimir and
Oguz, Barlas and
Lewis, Mike and
Yih, Wen-tau and
Riedel, Sebastian",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.529/",
doi = "10.18653/v1/2021.acl-long.529",
pages = "6787--6799"
}