Bilingual Tabular Inference: A Case Study on Indic Languages

Chaitanya Agarwal, Vivek Gupta, Anoop Kunchukuttan, Manish Shrivastava


Abstract
Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language. However, due to the uneven distribution of text resources on the web across languages, it is common to have the tabular premise in a high resource language and the hypothesis in a low resource language. As a result, we present the challenging task of bilingual Tabular Natural Language Inference (bTNLI), in which the tabular premise and a hypothesis over it are in two separate languages. We construct EI-InfoTabS: an English-Indic bTNLI dataset by translating the textual hypotheses of the English TNLI dataset InfoTabS into eleven major Indian languages. We thoroughly investigate how pre-trained multilingual models learn and perform on EI-InfoTabS. Our study shows that the performance on bTNLI can be close to its monolingual counterpart, with translate-train, translate-test and unified-train being strongly competitive baselines.
Anthology ID:
2022.naacl-main.295
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4018–4037
Language:
URL:
https://aclanthology.org/2022.naacl-main.295
DOI:
10.18653/v1/2022.naacl-main.295
Bibkey:
Cite (ACL):
Chaitanya Agarwal, Vivek Gupta, Anoop Kunchukuttan, and Manish Shrivastava. 2022. Bilingual Tabular Inference: A Case Study on Indic Languages. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4018–4037, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Bilingual Tabular Inference: A Case Study on Indic Languages (Agarwal et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.295.pdf
Data
TabFact