<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gupta-etal-2021-unsupervised">
    <titleInfo>
        <title>Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Akshat</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sargam</namePart>
        <namePart type="family">Menghani</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sai</namePart>
        <namePart type="given">Krishna</namePart>
        <namePart type="family">Rallabandi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alan</namePart>
        <namePart type="given">W</namePart>
        <namePart type="family">Black</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Thamar</namePart>
            <namePart type="family">Solorio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shuguang</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="given">W</namePart>
            <namePart type="family">Black</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mona</namePart>
            <namePart type="family">Diab</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunayana</namePart>
            <namePart type="family">Sitaram</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Victor</namePart>
            <namePart type="family">Soto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Emre</namePart>
            <namePart type="family">Yilmaz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anirudh</namePart>
            <namePart type="family">Srinivasan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a large amount of social media text is characterized by the presence of Code-Switching. Thus, it has become important to build models that can handle code-switched data. However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms. We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data. We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer. We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question - ‘Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?’. Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7% (weighted F1 scores) when compared to supervised models trained for a two class problem.</abstract>
    <identifier type="citekey">gupta-etal-2021-unsupervised</identifier>
    <identifier type="doi">10.18653/v1/2021.calcs-1.13</identifier>
    <location>
        <url>https://aclanthology.org/2021.calcs-1.13</url>
    </location>
    <part>
        <date>2021-06</date>
        <extent unit="page">
            <start>103</start>
            <end>112</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data
%A Gupta, Akshat
%A Menghani, Sargam
%A Rallabandi, Sai Krishna
%A Black, Alan W.
%Y Solorio, Thamar
%Y Chen, Shuguang
%Y Black, Alan W.
%Y Diab, Mona
%Y Sitaram, Sunayana
%Y Soto, Victor
%Y Yilmaz, Emre
%Y Srinivasan, Anirudh
%S Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F gupta-etal-2021-unsupervised
%X Sentiment analysis is an important task in understanding social media content like customer reviews, Twitter and Facebook feeds etc. In multilingual communities around the world, a large amount of social media text is characterized by the presence of Code-Switching. Thus, it has become important to build models that can handle code-switched data. However, annotated code-switched data is scarce and there is a need for unsupervised models and algorithms. We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data. We use the power of pre-trained BERT models for initialization and fine-tune them in an unsupervised manner, only using pseudo labels produced by zero-shot transfer. We test our algorithm on multiple code-switched languages and provide a detailed analysis of the learning dynamics of the algorithm with the aim of answering the question - ‘Does our unsupervised model understand the Code-Switched languages or does it just learn its representations?’. Our unsupervised models compete well with their supervised counterparts, with their performance reaching within 1-7% (weighted F1 scores) when compared to supervised models trained for a two class problem.
%R 10.18653/v1/2021.calcs-1.13
%U https://aclanthology.org/2021.calcs-1.13
%U https://doi.org/10.18653/v1/2021.calcs-1.13
%P 103-112

Markdown (Informal)

[Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data](https://aclanthology.org/2021.calcs-1.13) (Gupta et al., CALCS 2021)

• Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data (Gupta et al., CALCS 2021)

ACL

• Akshat Gupta, Sargam Menghani, Sai Krishna Rallabandi, and Alan W Black. 2021. Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data. In Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching, pages 103–112, Online. Association for Computational Linguistics.