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        <title>(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus</title>
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    <name type="personal">
        <namePart type="given">Satoru</namePart>
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        <namePart type="given">Mamoru</namePart>
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            <title>Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications</title>
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        <name type="personal">
            <namePart type="given">Helen</namePart>
            <namePart type="family">Yannakoudakis</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
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        <name type="personal">
            <namePart type="given">Claudia</namePart>
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        <name type="personal">
            <namePart type="given">Nitin</namePart>
            <namePart type="family">Madnani</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Ildikó</namePart>
            <namePart type="family">Pilán</namePart>
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        <name type="personal">
            <namePart type="given">Torsten</namePart>
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    <abstract>We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on a low resource track of the shared task at BEA2019. As a result, we achieved an F0.5 score of 28.31 points with the test data.</abstract>
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    <identifier type="doi">10.18653/v1/W19-4413</identifier>
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%0 Conference Proceedings
%T (Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus
%A Katsumata, Satoru
%A Komachi, Mamoru
%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 katsumata-komachi-2019-almost
%X We introduce unsupervised techniques based on phrase-based statistical machine translation for grammatical error correction (GEC) trained on a pseudo learner corpus created by Google Translation. We verified our GEC system through experiments on a low resource track of the shared task at BEA2019. As a result, we achieved an F0.5 score of 28.31 points with the test data.
%R 10.18653/v1/W19-4413
%U https://aclanthology.org/W19-4413
%U https://doi.org/10.18653/v1/W19-4413
%P 134-138

Markdown (Informal)

[(Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus](https://aclanthology.org/W19-4413) (Katsumata & Komachi, BEA 2019)

• (Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus (Katsumata & Komachi, BEA 2019)

ACL

• Satoru Katsumata and Mamoru Komachi. 2019. (Almost) Unsupervised Grammatical Error Correction using Synthetic Comparable Corpus. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 134–138, Florence, Italy. Association for Computational Linguistics.