@inproceedings{aggarwal2022indicxnli,
author = {Aggarwal, Divyanshu and Gupta, Vivek and Kunchukuttan, Anoop},
title = {IndicXNLI: Evaluating Multilingual Inference for Indian Languages},
booktitle = {EMNLP 2022},
year = {2022},
month = {April},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/indicxnli-evaluating-multilingual-inference-for-indian-languages/},
}