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    <titleInfo>
        <title>CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction</title>
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    <name type="personal">
        <namePart type="given">Jakub</namePart>
        <namePart type="family">Náplava</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Milan</namePart>
        <namePart type="family">Straka</namePart>
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        <titleInfo>
            <title>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Claudia</namePart>
            <namePart type="family">Leacock</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nitin</namePart>
            <namePart type="family">Madnani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ildikó</namePart>
            <namePart type="family">Pilán</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Torsten</namePart>
            <namePart type="family">Zesch</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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                <placeTerm type="text">Florence, Italy</placeTerm>
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    <abstract>Our submitted models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations. The system in the Restricted Track is trained on the provided corpora with oversampled “cleaner” sentences and reaches 59.39 F0.5 score on the test set. The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score.</abstract>
    <identifier type="citekey">naplava-straka-2019-cuni</identifier>
    <identifier type="doi">10.18653/v1/W19-4419</identifier>
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        <url>https://aclanthology.org/W19-4419</url>
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    <part>
        <date>2019-08</date>
        <extent unit="page">
            <start>183</start>
            <end>190</end>
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%0 Conference Proceedings
%T CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction
%A Náplava, Jakub
%A Straka, Milan
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F naplava-straka-2019-cuni
%X Our submitted models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations. The system in the Restricted Track is trained on the provided corpora with oversampled “cleaner” sentences and reaches 59.39 F0.5 score on the test set. The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score.
%R 10.18653/v1/W19-4419
%U https://aclanthology.org/W19-4419
%U https://doi.org/10.18653/v1/W19-4419
%P 183-190

Markdown (Informal)

[CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction](https://aclanthology.org/W19-4419) (Náplava & Straka, BEA 2019)

• CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction (Náplava & Straka, BEA 2019)

ACL

• Jakub Náplava and Milan Straka. 2019. CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 183–190, Florence, Italy. Association for Computational Linguistics.