Table-based Fact Verification With Salience-aware Learning

Fei Wang, Kexuan Sun, Jay Pujara, Pedro Szekely, Muhao Chen


Abstract
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover, training a generalized fact verification model requires abundant labeled training data. In this paper, we propose a novel system to address these problems. Inspired by counterfactual causality, our system identifies token-level salience in the statement with probing-based salience estimation. Salience estimation allows enhanced learning of fact verification from two perspectives. From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement. From the other perspective, our system applies salience-aware data augmentation to generate a more diverse set of training instances by replacing non-salient terms. Experimental results on TabFact show the effective improvement by the proposed salience-aware learning techniques, leading to the new SOTA performance on the benchmark.
Anthology ID:
2021.findings-emnlp.338
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4025–4036
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.338
DOI:
10.18653/v1/2021.findings-emnlp.338
Bibkey:
Cite (ACL):
Fei Wang, Kexuan Sun, Jay Pujara, Pedro Szekely, and Muhao Chen. 2021. Table-based Fact Verification With Salience-aware Learning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4025–4036, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Table-based Fact Verification With Salience-aware Learning (Wang et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.338.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.338.mp4
Code
 luka-group/salience-aware-learning
Data
TabFact