<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gupta-etal-2023-mutant">
    <titleInfo>
        <title>MUTANT: A Multi-sentential Code-mixed Hinglish Dataset</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rahul</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vivek</namePart>
        <namePart type="family">Srivastava</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mayank</namePart>
        <namePart type="family">Singh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EACL 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dubrovnik, Croatia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The multi-sentential long sequence textual data unfolds several interesting research directions pertaining to natural language processing and generation. Though we observe several high-quality long-sequence datasets for English and other monolingual languages, there is no significant effort in building such resources for code-mixed languages such as Hinglish (code-mixing of Hindi-English). In this paper, we propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i.e., MUTANT. We propose a token-level language-aware pipeline and extend the existing metrics measuring the degree of code-mixing to a multi-sentential framework and automatically identify MCT in the multilingual articles. The MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs. To facilitate future research directions, we will make the dataset and the code publicly available upon publication.</abstract>
    <identifier type="citekey">gupta-etal-2023-mutant</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.56</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-eacl.56</url>
    </location>
    <part>
        <date>2023-05</date>
        <extent unit="page">
            <start>744</start>
            <end>753</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MUTANT: A Multi-sentential Code-mixed Hinglish Dataset
%A Gupta, Rahul
%A Srivastava, Vivek
%A Singh, Mayank
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F gupta-etal-2023-mutant
%X The multi-sentential long sequence textual data unfolds several interesting research directions pertaining to natural language processing and generation. Though we observe several high-quality long-sequence datasets for English and other monolingual languages, there is no significant effort in building such resources for code-mixed languages such as Hinglish (code-mixing of Hindi-English). In this paper, we propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i.e., MUTANT. We propose a token-level language-aware pipeline and extend the existing metrics measuring the degree of code-mixing to a multi-sentential framework and automatically identify MCT in the multilingual articles. The MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs. To facilitate future research directions, we will make the dataset and the code publicly available upon publication.
%R 10.18653/v1/2023.findings-eacl.56
%U https://aclanthology.org/2023.findings-eacl.56
%U https://doi.org/10.18653/v1/2023.findings-eacl.56
%P 744-753

Markdown (Informal)

[MUTANT: A Multi-sentential Code-mixed Hinglish Dataset](https://aclanthology.org/2023.findings-eacl.56) (Gupta et al., Findings 2023)

• MUTANT: A Multi-sentential Code-mixed Hinglish Dataset (Gupta et al., Findings 2023)

ACL

• Rahul Gupta, Vivek Srivastava, and Mayank Singh. 2023. MUTANT: A Multi-sentential Code-mixed Hinglish Dataset. In Findings of the Association for Computational Linguistics: EACL 2023, pages 744–753, Dubrovnik, Croatia. Association for Computational Linguistics.