<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="aggarwal-etal-2022-indicxnli">
    <titleInfo>
        <title>IndicXNLI: Evaluating Multilingual Inference for Indian Languages</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Divyanshu</namePart>
        <namePart type="family">Aggarwal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vivek</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anoop</namePart>
        <namePart type="family">Kunchukuttan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">aggarwal-etal-2022-indicxnli</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.755</identifier>
    <location>
        <url>https://aclanthology.org/2022.emnlp-main.755/</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>10994</start>
            <end>11006</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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.