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        <title>Automatic Estimation of Simultaneous Interpreter Performance</title>
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    <name type="personal">
        <namePart type="given">Craig</namePart>
        <namePart type="family">Stewart</namePart>
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    <name type="personal">
        <namePart type="given">Nikolai</namePart>
        <namePart type="family">Vogler</namePart>
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    <name type="personal">
        <namePart type="given">Junjie</namePart>
        <namePart type="family">Hu</namePart>
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    <name type="personal">
        <namePart type="given">Jordan</namePart>
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    <name type="personal">
        <namePart type="given">Graham</namePart>
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            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short 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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    <abstract>Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.</abstract>
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    <identifier type="doi">10.18653/v1/P18-2105</identifier>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Automatic Estimation of Simultaneous Interpreter Performance
%A Stewart, Craig
%A Vogler, Nikolai
%A Hu, Junjie
%A Boyd-Graber, Jordan
%A Neubig, Graham
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F stewart-etal-2018-automatic
%X Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.
%R 10.18653/v1/P18-2105
%U https://aclanthology.org/P18-2105
%U https://doi.org/10.18653/v1/P18-2105
%P 662-666

Markdown (Informal)

[Automatic Estimation of Simultaneous Interpreter Performance](https://aclanthology.org/P18-2105) (Stewart et al., ACL 2018)

• Automatic Estimation of Simultaneous Interpreter Performance (Stewart et al., ACL 2018)

ACL

• Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, and Graham Neubig. 2018. Automatic Estimation of Simultaneous Interpreter Performance. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 662–666, Melbourne, Australia. Association for Computational Linguistics.