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        <title>Measuring Machine Translation Errors in New Domains</title>
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    <name type="personal">
        <namePart type="given">Ann</namePart>
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        <namePart type="given">Marine</namePart>
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    <name type="personal">
        <namePart type="given">Hal</namePart>
        <namePart type="family">Daumé III</namePart>
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    <name type="personal">
        <namePart type="given">Dragos</namePart>
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    <abstract>We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a Parliament-trained phrase-based machine translation system is applied in four very different domains: news, medical texts, scientific articles and movie subtitles. We present quantitative and qualitative experiments that highlight opportunities for future research in domain adaptation for machine translation.</abstract>
    <identifier type="citekey">irvine-etal-2013-measuring</identifier>
    <identifier type="doi">10.1162/tacl_a_00239</identifier>
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        <url>https://aclanthology.org/Q13-1035</url>
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        <date>2013</date>
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%0 Journal Article
%T Measuring Machine Translation Errors in New Domains
%A Irvine, Ann
%A Morgan, John
%A Carpuat, Marine
%A Daumé III, Hal
%A Munteanu, Dragos
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F irvine-etal-2013-measuring
%X We develop two techniques for analyzing the effect of porting a machine translation system to a new domain. One is a macro-level analysis that measures how domain shift affects corpus-level evaluation; the second is a micro-level analysis for word-level errors. We apply these methods to understand what happens when a Parliament-trained phrase-based machine translation system is applied in four very different domains: news, medical texts, scientific articles and movie subtitles. We present quantitative and qualitative experiments that highlight opportunities for future research in domain adaptation for machine translation.
%R 10.1162/tacl_a_00239
%U https://aclanthology.org/Q13-1035
%U https://doi.org/10.1162/tacl_a_00239
%P 429-440

Markdown (Informal)

[Measuring Machine Translation Errors in New Domains](https://aclanthology.org/Q13-1035) (Irvine et al., TACL 2013)

• Measuring Machine Translation Errors in New Domains (Irvine et al., TACL 2013)

ACL

• Ann Irvine, John Morgan, Marine Carpuat, Hal Daumé III, and Dragos Munteanu. 2013. Measuring Machine Translation Errors in New Domains. Transactions of the Association for Computational Linguistics, 1:429–440.