Table-based Fact Verification with Self-labeled Keypoint Alignment

Guangzhen Zhao, Peng Yang


Abstract
Table-based fact verification aims to verify whether a statement sentence is trusted or fake. Most existing methods rely on graph feature or data augmentation but fail to investigate evidence correlation between the statement and table effectively. In this paper, we propose a self-Labeled Keypoint Alignment model, named LKA, to explore the correlation between the two. Specifically, a dual-view alignment module based on the statement and table views is designed to discriminate the salient words through multiple interactions, where one regular and one adversarial alignment network cooperatively character the alignment discrepancy. Considering the interaction characteristic inherent in the alignment module, we introduce a novel mixture-of experts block to elaborately integrate the interacted information for supporting the alignment and final classification. Furthermore, a contrastive learning loss is utilized to learn the precise representation of the structure-involved words, encouraging the words closer to words with the same table attribute and farther from the words with the unrelated attribute. Experimental results on three widely-studied datasets show that our model can outperform the state-of-the-art baselines and capture interpretable evidence words.
Anthology ID:
2022.coling-1.120
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1401–1411
Language:
URL:
https://aclanthology.org/2022.coling-1.120
DOI:
Bibkey:
Cite (ACL):
Guangzhen Zhao and Peng Yang. 2022. Table-based Fact Verification with Self-labeled Keypoint Alignment. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1401–1411, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Table-based Fact Verification with Self-labeled Keypoint Alignment (Zhao & Yang, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.120.pdf
Data
TabFact