<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2020-seqvat">
    <titleInfo>
        <title>SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Luoxin</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Weitong</namePart>
        <namePart type="family">Ruan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xinyue</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jianhua</namePart>
        <namePart type="family">Lu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.</abstract>
    <identifier type="citekey">chen-etal-2020-seqvat</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.777</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.777</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>8801</start>
            <end>8811</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling
%A Chen, Luoxin
%A Ruan, Weitong
%A Liu, Xinyue
%A Lu, Jianhua
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-seqvat
%X Virtual adversarial training (VAT) is a powerful technique to improve model robustness in both supervised and semi-supervised settings. It is effective and can be easily adopted on lots of image classification and text classification tasks. However, its benefits to sequence labeling tasks such as named entity recognition (NER) have not been shown as significant, mostly, because the previous approach can not combine VAT with the conditional random field (CRF). CRF can significantly boost accuracy for sequence models by putting constraints on label transitions, which makes it an essential component in most state-of-the-art sequence labeling model architectures. In this paper, we propose SeqVAT, a method which naturally applies VAT to sequence labeling models with CRF. Empirical studies show that SeqVAT not only significantly improves the sequence labeling performance over baselines under supervised settings, but also outperforms state-of-the-art approaches under semi-supervised settings.
%R 10.18653/v1/2020.acl-main.777
%U https://aclanthology.org/2020.acl-main.777
%U https://doi.org/10.18653/v1/2020.acl-main.777
%P 8801-8811

Markdown (Informal)

[SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling](https://aclanthology.org/2020.acl-main.777) (Chen et al., ACL 2020)

• SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (Chen et al., ACL 2020)

ACL

• Luoxin Chen, Weitong Ruan, Xinyue Liu, and Jianhua Lu. 2020. SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8801–8811, Online. Association for Computational Linguistics.