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    <titleInfo>
        <title>Automatic Metric Validation for Grammatical Error Correction</title>
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    <name type="personal">
        <namePart type="given">Leshem</namePart>
        <namePart type="family">Choshen</namePart>
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        <namePart type="given">Omri</namePart>
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        <dateIssued>2018-07</dateIssued>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties in the existing methodology. Experiments with MAEGE shed a new light on metric quality, showing for example that the standard M² metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that some types of valid edits are consistently penalized by existing metrics.</abstract>
    <identifier type="citekey">choshen-abend-2018-automatic</identifier>
    <identifier type="doi">10.18653/v1/P18-1127</identifier>
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        <url>https://aclanthology.org/P18-1127</url>
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%0 Conference Proceedings
%T Automatic Metric Validation for Grammatical Error Correction
%A Choshen, Leshem
%A Abend, Omri
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F choshen-abend-2018-automatic
%X Metric validation in Grammatical Error Correction (GEC) is currently done by observing the correlation between human and metric-induced rankings. However, such correlation studies are costly, methodologically troublesome, and suffer from low inter-rater agreement. We propose MAEGE, an automatic methodology for GEC metric validation, that overcomes many of the difficulties in the existing methodology. Experiments with MAEGE shed a new light on metric quality, showing for example that the standard M² metric fares poorly on corpus-level ranking. Moreover, we use MAEGE to perform a detailed analysis of metric behavior, showing that some types of valid edits are consistently penalized by existing metrics.
%R 10.18653/v1/P18-1127
%U https://aclanthology.org/P18-1127
%U https://doi.org/10.18653/v1/P18-1127
%P 1372-1382

Markdown (Informal)

[Automatic Metric Validation for Grammatical Error Correction](https://aclanthology.org/P18-1127) (Choshen & Abend, ACL 2018)

• Automatic Metric Validation for Grammatical Error Correction (Choshen & Abend, ACL 2018)

ACL

• Leshem Choshen and Omri Abend. 2018. Automatic Metric Validation for Grammatical Error Correction. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1372–1382, Melbourne, Australia. Association for Computational Linguistics.