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        <title>Multi-headed Architecture Based on BERT for Grammatical Errors Correction</title>
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    <name type="personal">
        <namePart type="given">Bohdan</namePart>
        <namePart type="family">Didenko</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>
            <namePart type="family">Leacock</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>In this paper, we describe our approach to GEC using the BERT model for creation of encoded representation and some of our enhancements, namely, “Heads” are fully-connected networks which are used for finding the errors and later receive recommendation from the networks on dealing with a highlighted part of the sentence only. Among the main advantages of our solution is increasing the system productivity and lowering the time of processing while keeping the high accuracy of GEC results.</abstract>
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    <identifier type="doi">10.18653/v1/W19-4426</identifier>
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%0 Conference Proceedings
%T Multi-headed Architecture Based on BERT for Grammatical Errors Correction
%A Didenko, Bohdan
%A Shaptala, Julia
%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 didenko-shaptala-2019-multi
%X In this paper, we describe our approach to GEC using the BERT model for creation of encoded representation and some of our enhancements, namely, “Heads” are fully-connected networks which are used for finding the errors and later receive recommendation from the networks on dealing with a highlighted part of the sentence only. Among the main advantages of our solution is increasing the system productivity and lowering the time of processing while keeping the high accuracy of GEC results.
%R 10.18653/v1/W19-4426
%U https://aclanthology.org/W19-4426
%U https://doi.org/10.18653/v1/W19-4426
%P 246-251

Markdown (Informal)

[Multi-headed Architecture Based on BERT for Grammatical Errors Correction](https://aclanthology.org/W19-4426) (Didenko & Shaptala, BEA 2019)

• Multi-headed Architecture Based on BERT for Grammatical Errors Correction (Didenko & Shaptala, BEA 2019)

ACL

• Bohdan Didenko and Julia Shaptala. 2019. Multi-headed Architecture Based on BERT for Grammatical Errors Correction. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 246–251, Florence, Italy. Association for Computational Linguistics.