Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification

Qi Shi, Yu Zhang, Qingyu Yin, Ting Liu


Abstract
Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table. Symbolic reasoning with logical operations plays a crucial role in this task. Existing methods leverage programs that contain rich logical information to enhance the verification process. However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations. To address the aforementioned problems, in this work, we formulate the table-based fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graph-based Verification network (LERGV). Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table. After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation. Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach.
Anthology ID:
2021.emnlp-main.16
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
175–184
Language:
URL:
https://aclanthology.org/2021.emnlp-main.16
DOI:
10.18653/v1/2021.emnlp-main.16
Bibkey:
Cite (ACL):
Qi Shi, Yu Zhang, Qingyu Yin, and Ting Liu. 2021. Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 175–184, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification (Shi et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.16.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.16.mp4
Code
 qshi95/lergv
Data
FEVERTabFact