IndicXNLI: Evaluating Multilingual Inference for Indian Languages

Divyanshu Aggarwal, Vivek Gupta, Anoop Kunchukuttan


Abstract
While Indic NLP has made rapid advances recently in terms of the availability of corpora and pre-trained models, benchmark datasets on standard NLU tasks are limited. To this end, we introduce INDICXNLI, an NLI dataset for 11 Indic languages. It has been created by high-quality machine translation of the original English XNLI dataset and our analysis attests to the quality of INDICXNLI. By finetuning different pre-trained LMs on this INDICXNLI, we analyze various cross-lingual transfer techniques with respect to the impact of the choice of language models, languages, multi-linguality, mix-language input, etc. These experiments provide us with useful insights into the behaviour of pre-trained models for a diverse set of languages.
Anthology ID:
2022.emnlp-main.755
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
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Pages:
10994–11006
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URL:
https://aclanthology.org/2022.emnlp-main.755
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Cite (ACL):
Divyanshu Aggarwal, Vivek Gupta, and Anoop Kunchukuttan. 2022. IndicXNLI: Evaluating Multilingual Inference for Indian Languages. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10994–11006, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
IndicXNLI: Evaluating Multilingual Inference for Indian Languages (Aggarwal et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/author-url/2022.emnlp-main.755.pdf