@article{irvine-etal-2013-measuring,
    title = "Measuring Machine Translation Errors in New Domains",
    author = "Irvine, Ann  and
      Morgan, John  and
      Carpuat, Marine  and
      Daum{\'e} III, Hal  and
      Munteanu, Dragos",
    editor = "Lin, Dekang  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "1",
    year = "2013",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/Q13-1035/",
    doi = "10.1162/tacl_a_00239",
    pages = "429--440",
    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."
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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)
ACL