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        <title>Artificial Error Generation with Machine Translation and Syntactic Patterns</title>
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        <namePart type="given">Marek</namePart>
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    <name type="personal">
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            <title>Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications</title>
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            <namePart type="given">Joel</namePart>
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            <namePart type="given">Jill</namePart>
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            <namePart type="given">Helen</namePart>
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    <abstract>Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.</abstract>
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%0 Conference Proceedings
%T Artificial Error Generation with Machine Translation and Syntactic Patterns
%A Rei, Marek
%A Felice, Mariano
%A Yuan, Zheng
%A Briscoe, Ted
%Y Tetreault, Joel
%Y Burstein, Jill
%Y Leacock, Claudia
%Y Yannakoudakis, Helen
%S Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F rei-etal-2017-artificial
%X Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.
%R 10.18653/v1/W17-5032
%U https://aclanthology.org/W17-5032
%U https://doi.org/10.18653/v1/W17-5032
%P 287-292

Markdown (Informal)

[Artificial Error Generation with Machine Translation and Syntactic Patterns](https://aclanthology.org/W17-5032) (Rei et al., BEA 2017)

• Artificial Error Generation with Machine Translation and Syntactic Patterns (Rei et al., BEA 2017)

ACL

• Marek Rei, Mariano Felice, Zheng Yuan, and Ted Briscoe. 2017. Artificial Error Generation with Machine Translation and Syntactic Patterns. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 287–292, Copenhagen, Denmark. Association for Computational Linguistics.