<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-huang-2022-unsupervised">
    <titleInfo>
        <title>Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuexin</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiaolei</namePart>
        <namePart type="family">Huang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th Joint Conference on Lexical and Computational Semantics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Vivi</namePart>
            <namePart type="family">Nastase</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ellie</namePart>
            <namePart type="family">Pavlick</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohammad</namePart>
            <namePart type="given">Taher</namePart>
            <namePart type="family">Pilehvar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jose</namePart>
            <namePart type="family">Camacho-Collados</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alessandro</namePart>
            <namePart type="family">Raganato</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Seattle, Washington</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Class imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced data. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains.</abstract>
    <identifier type="citekey">wu-huang-2022-unsupervised</identifier>
    <identifier type="doi">10.18653/v1/2022.starsem-1.27</identifier>
    <location>
        <url>https://aclanthology.org/2022.starsem-1.27</url>
    </location>
    <part>
        <date>2022-07</date>
        <extent unit="page">
            <start>311</start>
            <end>322</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification
%A Wu, Yuexin
%A Huang, Xiaolei
%Y Nastase, Vivi
%Y Pavlick, Ellie
%Y Pilehvar, Mohammad Taher
%Y Camacho-Collados, Jose
%Y Raganato, Alessandro
%S Proceedings of the 11th Joint Conference on Lexical and Computational Semantics
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, Washington
%F wu-huang-2022-unsupervised
%X Class imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced data. In this work, we propose an unsupervised domain adaptation approach via reinforcement learning that jointly leverages feature variants and imbalanced labels across domains. We experiment with the text classification task for its easily accessible datasets and compare the proposed method with five baselines. Experiments on three datasets prove that our proposed method can effectively learn robust domain-invariant representations and successfully adapt text classifiers on imbalanced classes over domains.
%R 10.18653/v1/2022.starsem-1.27
%U https://aclanthology.org/2022.starsem-1.27
%U https://doi.org/10.18653/v1/2022.starsem-1.27
%P 311-322

Markdown (Informal)

[Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification](https://aclanthology.org/2022.starsem-1.27) (Wu & Huang, *SEM 2022)

• Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification (Wu & Huang, *SEM 2022)

ACL

• Yuexin Wu and Xiaolei Huang. 2022. Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification. In Proceedings of the 11th Joint Conference on Lexical and Computational Semantics, pages 311–322, Seattle, Washington. Association for Computational Linguistics.