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    <titleInfo>
        <title>Error Detection for Post-editing Rule-based Machine Translation</title>
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    <name type="personal">
        <namePart type="given">Justina</namePart>
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    <name type="personal">
        <namePart type="given">Munshi</namePart>
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        <dateIssued>2012-oct 28</dateIssued>
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            <title>Workshop on Post-Editing Technology and Practice</title>
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            <namePart type="given">Sharon</namePart>
            <namePart type="family">O’Brien</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Michel</namePart>
            <namePart type="family">Simard</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>The increasing role of post-editing as a way of improving machine translation output and a faster alternative to translating from scratch has lately attracted researchers’ attention and various attempts have been proposed to facilitate the task. We experiment with a method to provide support for the post-editing task through error detection. A deep linguistic error analysis was done of a sample of English sentences translated from Portuguese by two Rule-based Machine Translation systems. We designed a set of rules to deal with various systematic translation errors and implemented a subset of these rules covering the errors of tense and number. The evaluation of these rules showed a satisfactory performance. In addition, we performed an experiment with human translators which confirmed that highlighting translation errors during the post-editing can help the translators perform the post-editing task up to 12 seconds per error faster and improve their efficiency by minimizing the number of missed errors.</abstract>
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        <url>https://aclanthology.org/2012.amta-wptp.9</url>
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%0 Conference Proceedings
%T Error Detection for Post-editing Rule-based Machine Translation
%A Valotkaite, Justina
%A Asadullah, Munshi
%Y O’Brien, Sharon
%Y Simard, Michel
%Y Specia, Lucia
%S Workshop on Post-Editing Technology and Practice
%D 2012
%8 oct 28
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F valotkaite-asadullah-2012-error
%X The increasing role of post-editing as a way of improving machine translation output and a faster alternative to translating from scratch has lately attracted researchers’ attention and various attempts have been proposed to facilitate the task. We experiment with a method to provide support for the post-editing task through error detection. A deep linguistic error analysis was done of a sample of English sentences translated from Portuguese by two Rule-based Machine Translation systems. We designed a set of rules to deal with various systematic translation errors and implemented a subset of these rules covering the errors of tense and number. The evaluation of these rules showed a satisfactory performance. In addition, we performed an experiment with human translators which confirmed that highlighting translation errors during the post-editing can help the translators perform the post-editing task up to 12 seconds per error faster and improve their efficiency by minimizing the number of missed errors.
%U https://aclanthology.org/2012.amta-wptp.9

Markdown (Informal)

[Error Detection for Post-editing Rule-based Machine Translation](https://aclanthology.org/2012.amta-wptp.9) (Valotkaite & Asadullah, AMTA 2012)

• Error Detection for Post-editing Rule-based Machine Translation (Valotkaite & Asadullah, AMTA 2012)

ACL

• Justina Valotkaite and Munshi Asadullah. 2012. Error Detection for Post-editing Rule-based Machine Translation. In Workshop on Post-Editing Technology and Practice, San Diego, California, USA. Association for Machine Translation in the Americas.