<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2022-emocaps">
    <titleInfo>
        <title>EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Zaijing</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fengxiao</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ming</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yusen</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2022</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Emotion recognition in conversation (ERC) aims to analyze the speaker’s state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.</abstract>
    <identifier type="citekey">li-etal-2022-emocaps</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-acl.126</identifier>
    <location>
        <url>https://aclanthology.org/2022.findings-acl.126</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>1610</start>
            <end>1618</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition
%A Li, Zaijing
%A Tang, Fengxiao
%A Zhao, Ming
%A Zhu, Yusen
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F li-etal-2022-emocaps
%X Emotion recognition in conversation (ERC) aims to analyze the speaker’s state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors through the Emoformer structure and obtain the emotion classification results from a context analysis model. Through the experiments with two benchmark datasets, our model shows better performance than the existing state-of-the-art models.
%R 10.18653/v1/2022.findings-acl.126
%U https://aclanthology.org/2022.findings-acl.126
%U https://doi.org/10.18653/v1/2022.findings-acl.126
%P 1610-1618

Markdown (Informal)

[EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition](https://aclanthology.org/2022.findings-acl.126) (Li et al., Findings 2022)

• EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition (Li et al., Findings 2022)

ACL

• Zaijing Li, Fengxiao Tang, Ming Zhao, and Yusen Zhu. 2022. EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognition. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1610–1618, Dublin, Ireland. Association for Computational Linguistics.