<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bataa-wu-2019-investigation">
    <titleInfo>
        <title>An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Enkhbold</namePart>
        <namePart type="family">Bataa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joshua</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Text classification approaches have usually required task-specific model architectures and huge labeled datasets. Recently, thanks to the rise of text-based transfer learning techniques, it is possible to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. In this work we focus on Japanese and show the potential use of transfer learning techniques in text classification. Specifically, we perform binary and multi-class sentiment classification on the Rakuten product review and Yahoo movie review datasets. We show that transfer learning-based approaches perform better than task-specific models trained on 3 times as much data. Furthermore, these approaches perform just as well for language modeling pre-trained on only 1/30 of the data. We release our pre-trained models and code as open source.</abstract>
    <identifier type="citekey">bataa-wu-2019-investigation</identifier>
    <identifier type="doi">10.18653/v1/P19-1458</identifier>
    <location>
        <url>https://aclanthology.org/P19-1458</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>4652</start>
            <end>4657</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
%A Bataa, Enkhbold
%A Wu, Joshua
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bataa-wu-2019-investigation
%X Text classification approaches have usually required task-specific model architectures and huge labeled datasets. Recently, thanks to the rise of text-based transfer learning techniques, it is possible to pre-train a language model in an unsupervised manner and leverage them to perform effective on downstream tasks. In this work we focus on Japanese and show the potential use of transfer learning techniques in text classification. Specifically, we perform binary and multi-class sentiment classification on the Rakuten product review and Yahoo movie review datasets. We show that transfer learning-based approaches perform better than task-specific models trained on 3 times as much data. Furthermore, these approaches perform just as well for language modeling pre-trained on only 1/30 of the data. We release our pre-trained models and code as open source.
%R 10.18653/v1/P19-1458
%U https://aclanthology.org/P19-1458
%U https://doi.org/10.18653/v1/P19-1458
%P 4652-4657

Markdown (Informal)

[An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese](https://aclanthology.org/P19-1458) (Bataa & Wu, ACL 2019)

• An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese (Bataa & Wu, ACL 2019)

ACL

• Enkhbold Bataa and Joshua Wu. 2019. An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4652–4657, Florence, Italy. Association for Computational Linguistics.