@inproceedings{koksal-etal-2021-boun,
title = "{BOUN} at {S}em{E}val-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data",
author = {K{\"o}ksal, Abdullatif and
Y{\"u}ksel, Yusuf and
Y{\i}ld{\i}r{\i}m, Bekir and
{\"O}zg{\"u}r, Arzucan},
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.52",
doi = "10.18653/v1/2021.semeval-1.52",
pages = "431--437",
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.",
}
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%0 Conference Proceedings
%T BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data
%A Köksal, Abdullatif
%A Yüksel, Yusuf
%A Yıldırım, Bekir
%A Özgür, Arzucan
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F koksal-etal-2021-boun
%X 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.
%R 10.18653/v1/2021.semeval-1.52
%U https://aclanthology.org/2021.semeval-1.52
%U https://doi.org/10.18653/v1/2021.semeval-1.52
%P 431-437
Markdown (Informal)
[BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data](https://aclanthology.org/2021.semeval-1.52) (Köksal et al., SemEval 2021)
ACL