<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2021-generative-adversarial">
    <titleInfo>
        <title>Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuh-Shyang</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chao-Yi</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lung-Hao</namePart>
        <namePart type="family">Lee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lung-Hao</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chia-Hui</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kuan-Yu</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)</publisher>
            <place>
                <placeTerm type="text">Taoyuan, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532.</abstract>
    <identifier type="citekey">wang-etal-2021-generative-adversarial</identifier>
    <location>
        <url>https://aclanthology.org/2021.rocling-1.36</url>
    </location>
    <part>
        <date>2021-10</date>
        <extent unit="page">
            <start>280</start>
            <end>285</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications
%A Wang, Yuh-Shyang
%A Chen, Chao-Yi
%A Lee, Lung-Hao
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F wang-etal-2021-generative-adversarial
%X We propose the mixed-attention-based Generative Adversarial Network (named maGAN), and apply it for citation intent classification in scientific publication. We select domain-specific training data, propose a mixed-attention mechanism, and employ generative adversarial network architecture for pre-training language model and fine-tuning to the downstream multi-class classification task. Experiments were conducted on the SciCite datasets to compare model performance. Our proposed maGAN model achieved the best Macro-F1 of 0.8532.
%U https://aclanthology.org/2021.rocling-1.36
%P 280-285

Markdown (Informal)

[Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications](https://aclanthology.org/2021.rocling-1.36) (Wang et al., ROCLING 2021)

• Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications (Wang et al., ROCLING 2021)

ACL

• Yuh-Shyang Wang, Chao-Yi Chen, and Lung-Hao Lee. 2021. Generative Adversarial Networks based on Mixed-Attentions for Citation Intent Classification in Scientific Publications. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 280–285, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).