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    <titleInfo>
        <title>Controlling Grammatical Error Correction Using Word Edit Rate</title>
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        <namePart type="given">Masahiro</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Mamoru</namePart>
        <namePart type="family">Komachi</namePart>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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            <namePart type="given">Daniel</namePart>
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    <abstract>When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods. The correction method depends on how much corrections a learner requires. In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction. We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate. Thereby, diverse corrected sentences is obtained from a single erroneous sentence. Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.</abstract>
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    <identifier type="doi">10.18653/v1/P19-2020</identifier>
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%0 Conference Proceedings
%T Controlling Grammatical Error Correction Using Word Edit Rate
%A Hotate, Kengo
%A Kaneko, Masahiro
%A Katsumata, Satoru
%A Komachi, Mamoru
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F hotate-etal-2019-controlling
%X When professional English teachers correct grammatically erroneous sentences written by English learners, they use various methods. The correction method depends on how much corrections a learner requires. In this paper, we propose a method for neural grammar error correction (GEC) that can control the degree of correction. We show that it is possible to actually control the degree of GEC by using new training data annotated with word edit rate. Thereby, diverse corrected sentences is obtained from a single erroneous sentence. Moreover, compared to a GEC model that does not use information on the degree of correction, the proposed method improves correction accuracy.
%R 10.18653/v1/P19-2020
%U https://aclanthology.org/P19-2020
%U https://doi.org/10.18653/v1/P19-2020
%P 149-154

Markdown (Informal)

[Controlling Grammatical Error Correction Using Word Edit Rate](https://aclanthology.org/P19-2020) (Hotate et al., ACL 2019)

• Controlling Grammatical Error Correction Using Word Edit Rate (Hotate et al., ACL 2019)

ACL

• Kengo Hotate, Masahiro Kaneko, Satoru Katsumata, and Mamoru Komachi. 2019. Controlling Grammatical Error Correction Using Word Edit Rate. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 149–154, Florence, Italy. Association for Computational Linguistics.