<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="koto-etal-2024-zero">
    <titleInfo>
        <title>Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Fajri</namePart>
        <namePart type="family">Koto</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tilman</namePart>
        <namePart type="family">Beck</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zeerak</namePart>
        <namePart type="family">Talat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Timothy</namePart>
        <namePart type="family">Baldwin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yvette</namePart>
            <namePart type="family">Graham</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Matthew</namePart>
            <namePart type="family">Purver</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">St. Julian’s, Malta</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT–3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages.</abstract>
    <identifier type="citekey">koto-etal-2024-zero</identifier>
    <location>
        <url>https://aclanthology.org/2024.eacl-long.18</url>
    </location>
    <part>
        <date>2024-03</date>
        <extent unit="page">
            <start>298</start>
            <end>320</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
%A Koto, Fajri
%A Beck, Tilman
%A Talat, Zeerak
%A Gurevych, Iryna
%A Baldwin, Timothy
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F koto-etal-2024-zero
%X Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages. In this paper, we relax the reliance on texts in low-resource languages by using multilingual lexicons in pretraining to enhance multilingual capabilities. Specifically, we focus on zero-shot sentiment analysis tasks across 34 languages, including 6 high/medium-resource languages, 25 low-resource languages, and 3 code-switching datasets. We demonstrate that pretraining using multilingual lexicons, without using any sentence-level sentiment data, achieves superior zero-shot performance compared to models fine-tuned on English sentiment datasets, and large language models like GPT–3.5, BLOOMZ, and XGLM. These findings are observable for unseen low-resource languages to code-mixed scenarios involving high-resource languages.
%U https://aclanthology.org/2024.eacl-long.18
%P 298-320

Markdown (Informal)

[Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon](https://aclanthology.org/2024.eacl-long.18) (Koto et al., EACL 2024)

• Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon (Koto et al., EACL 2024)

ACL

• Fajri Koto, Tilman Beck, Zeerak Talat, Iryna Gurevych, and Timothy Baldwin. 2024. Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 298–320, St. Julian’s, Malta. Association for Computational Linguistics.