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    <titleInfo>
        <title>Using Wikipedia Edits in Low Resource Grammatical Error Correction</title>
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    <name type="personal">
        <namePart type="given">Adriane</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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            <namePart type="given">Wei</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6111</identifier>
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%0 Conference Proceedings
%T Using Wikipedia Edits in Low Resource Grammatical Error Correction
%A Boyd, Adriane
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F boyd-2018-using
%X We develop a grammatical error correction (GEC) system for German using a small gold GEC corpus augmented with edits extracted from Wikipedia revision history. We extend the automatic error annotation tool ERRANT (Bryant et al., 2017) for German and use it to analyze both gold GEC corrections and Wikipedia edits (Grundkiewicz and Junczys-Dowmunt, 2014) in order to select as additional training data Wikipedia edits containing grammatical corrections similar to those in the gold corpus. Using a multilayer convolutional encoder-decoder neural network GEC approach (Chollampatt and Ng, 2018), we evaluate the contribution of Wikipedia edits and find that carefully selected Wikipedia edits increase performance by over 5%.
%R 10.18653/v1/W18-6111
%U https://aclanthology.org/W18-6111/
%U https://doi.org/10.18653/v1/W18-6111
%P 79-84

Markdown (Informal)

[Using Wikipedia Edits in Low Resource Grammatical Error Correction](https://aclanthology.org/W18-6111/) (Boyd, WNUT 2018)

• Using Wikipedia Edits in Low Resource Grammatical Error Correction (Boyd, WNUT 2018)

ACL

• Adriane Boyd. 2018. Using Wikipedia Edits in Low Resource Grammatical Error Correction. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 79–84, Brussels, Belgium. Association for Computational Linguistics.