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    <titleInfo>
        <title>Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection</title>
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    <name type="personal">
        <namePart type="given">Sudhanshu</namePart>
        <namePart type="family">Kasewa</namePart>
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    <name type="personal">
        <namePart type="given">Pontus</namePart>
        <namePart type="family">Stenetorp</namePart>
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    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Riedel</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturally-occurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.</abstract>
    <identifier type="citekey">kasewa-etal-2018-wronging</identifier>
    <identifier type="doi">10.18653/v1/D18-1541</identifier>
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        <url>https://aclanthology.org/D18-1541/</url>
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    <part>
        <date>2018-oct-nov</date>
        <extent unit="page">
            <start>4977</start>
            <end>4983</end>
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%0 Conference Proceedings
%T Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
%A Kasewa, Sudhanshu
%A Stenetorp, Pontus
%A Riedel, Sebastian
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kasewa-etal-2018-wronging
%X Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturally-occurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% F0.5 score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 F1 score, indicating that our model generates mostly human-like instances.
%R 10.18653/v1/D18-1541
%U https://aclanthology.org/D18-1541/
%U https://doi.org/10.18653/v1/D18-1541
%P 4977-4983

Markdown (Informal)

[Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection](https://aclanthology.org/D18-1541/) (Kasewa et al., EMNLP 2018)

• Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection (Kasewa et al., EMNLP 2018)

ACL

• Sudhanshu Kasewa, Pontus Stenetorp, and Sebastian Riedel. 2018. Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4977–4983, Brussels, Belgium. Association for Computational Linguistics.