<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2022-crowner">
    <titleInfo>
        <title>CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Qiu-Xia</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Te-Yu</namePart>
        <namePart type="family">Chi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Te-Lun</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jyh-Shing</namePart>
        <namePart type="given">Roger</namePart>
        <namePart type="family">Jang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <language>
        <languageTerm type="text">Chinese</languageTerm>
        <languageTerm type="code" authority="iso639-2b">chi</languageTerm>
    </language>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yung-Chun</namePart>
            <namePart type="family">Chang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yi-Chin</namePart>
            <namePart type="family">Huang</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">Taipei, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This study uses training and validation data from the “ROCLING 2022 Chinese Health Care Named Entity Recognition Task” for modeling. The modeling process adopts technologies such as data augmentation and data post-processing, and uses the MacBERT pre-training model to build a dedicated Chinese medical field NER recognizer. During the fine-tuning process, we also added adversarial training methods, such as FGM and PGD, and the results of the final tuned model were close to the best team for task evaluation. In addition, by introducing mixed-precision training, we also greatly reduce the time cost of training.</abstract>
    <identifier type="citekey">zhang-etal-2022-crowner</identifier>
    <location>
        <url>https://aclanthology.org/2022.rocling-1.40</url>
    </location>
    <part>
        <date>2022-11</date>
        <extent unit="page">
            <start>321</start>
            <end>328</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training
%A Zhang, Qiu-Xia
%A Chi, Te-Yu
%A Yang, Te-Lun
%A Jang, Jyh-Shing Roger
%Y Chang, Yung-Chun
%Y Huang, Yi-Chin
%S Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)
%D 2022
%8 November
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taipei, Taiwan
%G Chinese
%F zhang-etal-2022-crowner
%X This study uses training and validation data from the “ROCLING 2022 Chinese Health Care Named Entity Recognition Task” for modeling. The modeling process adopts technologies such as data augmentation and data post-processing, and uses the MacBERT pre-training model to build a dedicated Chinese medical field NER recognizer. During the fine-tuning process, we also added adversarial training methods, such as FGM and PGD, and the results of the final tuned model were close to the best team for task evaluation. In addition, by introducing mixed-precision training, we also greatly reduce the time cost of training.
%U https://aclanthology.org/2022.rocling-1.40
%P 321-328

Markdown (Informal)

[CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training](https://aclanthology.org/2022.rocling-1.40) (Zhang et al., ROCLING 2022)

• CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training (Zhang et al., ROCLING 2022)

ACL

• Qiu-Xia Zhang, Te-Yu Chi, Te-Lun Yang, and Jyh-Shing Roger Jang. 2022. CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training. In Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022), pages 321–328, Taipei, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).