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        <title>IndicXNLI: Evaluating Multilingual Inference for Indian Languages</title>
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    <name type="personal">
        <namePart type="given">Divyanshu</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T IndicXNLI: Evaluating Multilingual Inference for Indian Languages
%A Aggarwal, Divyanshu
%A Gupta, Vivek
%A Kunchukuttan, Anoop
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F aggarwal-etal-2022-indicxnli
%X 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.
%R 10.18653/v1/2022.emnlp-main.755
%U https://aclanthology.org/2022.emnlp-main.755
%U https://doi.org/10.18653/v1/2022.emnlp-main.755
%P 10994-11006

Markdown (Informal)

[IndicXNLI: Evaluating Multilingual Inference for Indian Languages](https://aclanthology.org/2022.emnlp-main.755) (Aggarwal et al., EMNLP 2022)

• IndicXNLI: Evaluating Multilingual Inference for Indian Languages (Aggarwal et al., EMNLP 2022)

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.