<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wen-etal-2023-learning">
    <titleInfo>
        <title>Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jintao</namePart>
        <namePart type="family">Wen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Geng</namePart>
        <namePart type="family">Tu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Rui</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dazhi</namePart>
        <namePart type="family">Jiang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wenhua</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>One-hot labels are commonly employed as ground truth in Emotion Recognition in Conversations (ERC). However, this approach may not fully encompass all the emotions conveyed in a single utterance, leading to suboptimal performance. Regrettably, current ERC datasets lack comprehensive emotionally distributed labels. To address this issue, we propose the Emotion Label Refinement (EmoLR) method, which utilizes context- and speaker-sensitive information to infer mixed emotional labels. EmoLR comprises an Emotion Predictor (EP) module and a Label Refinement (LR) module. The EP module recognizes emotions and provides context/speaker states for the LR module. Subsequently, the LR module calculates the similarity between these states and ground-truth labels, generating a refined label distribution (RLD). The RLD captures a more comprehensive range of emotions than the original one-hot labels. These refined labels are then used for model training in place of the one-hot labels. Experimental results on three public conversational datasets demonstrate that our EmoLR achieves state-of-the-art performance.</abstract>
    <identifier type="citekey">wen-etal-2023-learning</identifier>
    <identifier type="doi">10.1162/tacl_a_00614</identifier>
    <location>
        <url>https://aclanthology.org/2023.tacl-1.84</url>
    </location>
    <part>
        <date>2023</date>
        <detail type="volume"><number>11</number></detail>
        <extent unit="page">
            <start>1485</start>
            <end>1499</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations
%A Wen, Jintao
%A Tu, Geng
%A Li, Rui
%A Jiang, Dazhi
%A Zhu, Wenhua
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F wen-etal-2023-learning
%X One-hot labels are commonly employed as ground truth in Emotion Recognition in Conversations (ERC). However, this approach may not fully encompass all the emotions conveyed in a single utterance, leading to suboptimal performance. Regrettably, current ERC datasets lack comprehensive emotionally distributed labels. To address this issue, we propose the Emotion Label Refinement (EmoLR) method, which utilizes context- and speaker-sensitive information to infer mixed emotional labels. EmoLR comprises an Emotion Predictor (EP) module and a Label Refinement (LR) module. The EP module recognizes emotions and provides context/speaker states for the LR module. Subsequently, the LR module calculates the similarity between these states and ground-truth labels, generating a refined label distribution (RLD). The RLD captures a more comprehensive range of emotions than the original one-hot labels. These refined labels are then used for model training in place of the one-hot labels. Experimental results on three public conversational datasets demonstrate that our EmoLR achieves state-of-the-art performance.
%R 10.1162/tacl_a_00614
%U https://aclanthology.org/2023.tacl-1.84
%U https://doi.org/10.1162/tacl_a_00614
%P 1485-1499

Markdown (Informal)

[Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations](https://aclanthology.org/2023.tacl-1.84) (Wen et al., TACL 2023)

• Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations (Wen et al., TACL 2023)

ACL

• Jintao Wen, Geng Tu, Rui Li, Dazhi Jiang, and Wenhua Zhu. 2023. Learning More from Mixed Emotions: A Label Refinement Method for Emotion Recognition in Conversations. Transactions of the Association for Computational Linguistics, 11:1485–1499.