@inproceedings{hu-etal-2023-unifee,
title = "{U}nif{EE}: Unified Evidence Extraction for Fact Verification",
author = "Hu, Nan and
Wu, Zirui and
Lai, Yuxuan and
Zhang, Chen and
Feng, Yansong",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.82",
doi = "10.18653/v1/2023.eacl-main.82",
pages = "1150--1160",
abstract = "FEVEROUS is a fact extraction and verification task that requires systems to extract evidence of both sentences and table cells from a Wikipedia dump, then predict the veracity of the given claim accordingly. Existing works extract evidence in the two formats separately, ignoring potential connections between them. In this paper, we propose a Unified Evidence Extraction model (UnifEE), which uses a mixed evidence graph to extract the evidence in both formats. With the carefully-designed unified evidence graph, UnifEE allows evidence interactions among all candidates in both formats at similar granularity. Experiments show that, with information aggregated from related evidence candidates in the fusion graph, UnifEE can make better decisions about which evidence should be kept, especially for claims requiring multi-hop reasoning or a combination of tables and texts. Thus it outperforms all previous evidence extraction methods and brings significant improvement in the subsequent claim verification step.",
}
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<abstract>FEVEROUS is a fact extraction and verification task that requires systems to extract evidence of both sentences and table cells from a Wikipedia dump, then predict the veracity of the given claim accordingly. Existing works extract evidence in the two formats separately, ignoring potential connections between them. In this paper, we propose a Unified Evidence Extraction model (UnifEE), which uses a mixed evidence graph to extract the evidence in both formats. With the carefully-designed unified evidence graph, UnifEE allows evidence interactions among all candidates in both formats at similar granularity. Experiments show that, with information aggregated from related evidence candidates in the fusion graph, UnifEE can make better decisions about which evidence should be kept, especially for claims requiring multi-hop reasoning or a combination of tables and texts. Thus it outperforms all previous evidence extraction methods and brings significant improvement in the subsequent claim verification step.</abstract>
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%0 Conference Proceedings
%T UnifEE: Unified Evidence Extraction for Fact Verification
%A Hu, Nan
%A Wu, Zirui
%A Lai, Yuxuan
%A Zhang, Chen
%A Feng, Yansong
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F hu-etal-2023-unifee
%X FEVEROUS is a fact extraction and verification task that requires systems to extract evidence of both sentences and table cells from a Wikipedia dump, then predict the veracity of the given claim accordingly. Existing works extract evidence in the two formats separately, ignoring potential connections between them. In this paper, we propose a Unified Evidence Extraction model (UnifEE), which uses a mixed evidence graph to extract the evidence in both formats. With the carefully-designed unified evidence graph, UnifEE allows evidence interactions among all candidates in both formats at similar granularity. Experiments show that, with information aggregated from related evidence candidates in the fusion graph, UnifEE can make better decisions about which evidence should be kept, especially for claims requiring multi-hop reasoning or a combination of tables and texts. Thus it outperforms all previous evidence extraction methods and brings significant improvement in the subsequent claim verification step.
%R 10.18653/v1/2023.eacl-main.82
%U https://aclanthology.org/2023.eacl-main.82
%U https://doi.org/10.18653/v1/2023.eacl-main.82
%P 1150-1160
Markdown (Informal)
[UnifEE: Unified Evidence Extraction for Fact Verification](https://aclanthology.org/2023.eacl-main.82) (Hu et al., EACL 2023)
ACL
- Nan Hu, Zirui Wu, Yuxuan Lai, Chen Zhang, and Yansong Feng. 2023. UnifEE: Unified Evidence Extraction for Fact Verification. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1150–1160, Dubrovnik, Croatia. Association for Computational Linguistics.
