BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data

Abdullatif Köksal, Yusuf Yüksel, Bekir Yıldırım, Arzucan Özgür


Abstract
In this paper, we present our text augmentation based approach for the Table Statement Support Subtask (Phase A) of SemEval-2021 Task 9. We experiment with different text augmentation techniques such as back translation and synonym swapping using Word2Vec and WordNet. We show that text augmentation techniques lead to 2.5% improvement in F1 on the test set. Further, we investigate the impact of domain adaptation and joint learning on fact verification in tabular data by utilizing the SemTabFacts and TabFact datasets. We observe that joint learning improves the F1 scores on the SemTabFacts and TabFact test sets by 3.31% and 0.77%, respectively.
Anthology ID:
2021.semeval-1.52
Volume:
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Month:
August
Year:
2021
Address:
Online
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
431–437
Language:
URL:
https://aclanthology.org/2021.semeval-1.52
DOI:
10.18653/v1/2021.semeval-1.52
Bibkey:
Cite (ACL):
Abdullatif Köksal, Yusuf Yüksel, Bekir Yıldırım, and Arzucan Özgür. 2021. BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 431–437, Online. Association for Computational Linguistics.
Cite (Informal):
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data (Köksal et al., SemEval 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.semeval-1.52.pdf
Data
GLUESuperGLUETabFact