<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2023-grammatical">
    <titleInfo>
        <title>Grammatical Error Correction via Mixed-Grained Weighted Training</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jiahao</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Quan</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chiwei</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhendong</namePart>
        <namePart type="family">Mao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yongdong</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, but inherent discrepancies in data are neglected. In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations. To this end, we propose MainGEC, which designs token-level and sentence-level training weights based on inherent discrepancies therein, and then conducts mixed-grained weighted training to improve the training effect for GEC. Empirical evaluation shows that whether in the Seq2Seq or Seq2Edit manner, MainGEC achieves consistent and significant performance improvements on two benchmark datasets, demonstrating the effectiveness and superiority of the mixed-grained weighted training. Further ablation experiments verify the effectiveness of designed weights for both granularities in MainGEC.</abstract>
    <identifier type="citekey">li-etal-2023-grammatical</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.400</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.400</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>6027</start>
            <end>6037</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Grammatical Error Correction via Mixed-Grained Weighted Training
%A Li, Jiahao
%A Wang, Quan
%A Zhu, Chiwei
%A Mao, Zhendong
%A Zhang, Yongdong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-grammatical
%X The task of Grammatical Error Correction (GEC) aims to automatically correct grammatical errors in natural texts. Almost all previous works treat annotated training data equally, but inherent discrepancies in data are neglected. In this paper, the inherent discrepancies are manifested in two aspects, namely, accuracy of data annotation and diversity of potential annotations. To this end, we propose MainGEC, which designs token-level and sentence-level training weights based on inherent discrepancies therein, and then conducts mixed-grained weighted training to improve the training effect for GEC. Empirical evaluation shows that whether in the Seq2Seq or Seq2Edit manner, MainGEC achieves consistent and significant performance improvements on two benchmark datasets, demonstrating the effectiveness and superiority of the mixed-grained weighted training. Further ablation experiments verify the effectiveness of designed weights for both granularities in MainGEC.
%R 10.18653/v1/2023.findings-emnlp.400
%U https://aclanthology.org/2023.findings-emnlp.400
%U https://doi.org/10.18653/v1/2023.findings-emnlp.400
%P 6027-6037

Markdown (Informal)

[Grammatical Error Correction via Mixed-Grained Weighted Training](https://aclanthology.org/2023.findings-emnlp.400) (Li et al., Findings 2023)

• Grammatical Error Correction via Mixed-Grained Weighted Training (Li et al., Findings 2023)

ACL

• Jiahao Li, Quan Wang, Chiwei Zhu, Zhendong Mao, and Yongdong Zhang. 2023. Grammatical Error Correction via Mixed-Grained Weighted Training. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6027–6037, Singapore. Association for Computational Linguistics.