Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables

Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, Yansong Feng


Abstract
Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables. Existing works convert all candidate evidence into either sentences or tables, thus often failing to fully capture the rich context in their original format from the converted evidence, let alone the context information lost during conversion. In this paper, we propose a Dual Channel Unified Format fact verification model (DCUF), which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously. With carefully-designed evidence conversion and organization methods, DCUF makes the most of pre-trained table/language models to encourage each evidence piece to perform early and thorough interactions with other pieces in its original format. Experiments show that our model can make better use of existing pre-trained models to absorb evidence of two formats, thus outperforming previous works by a large margin. Our code and models are publicly available.
Anthology ID:
2022.naacl-main.384
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5232–5242
Language:
URL:
https://aclanthology.org/2022.naacl-main.384
DOI:
10.18653/v1/2022.naacl-main.384
Bibkey:
Cite (ACL):
Nan Hu, Zirui Wu, Yuxuan Lai, Xiao Liu, and Yansong Feng. 2022. Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5232–5242, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables (Hu et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.384.pdf
Data
FEVEROUS