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        <title>A fluency error categorization scheme to guide automated machine translation evaluation</title>
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        <namePart type="given">Debbie</namePart>
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    <abstract>Existing automated MT evaluation methods often require expert human translations. These are produced for every language pair evaluated and, due to this expense, subsequent evaluations tend to rely on the same texts, which do not necessarily reflect real MT use. In contrast, we are designing an automated MT evaluation system, intended for use by post-editors, purchasers and developers, that requires nothing but the raw MT output. Furthermore, our research is based on texts that reflect corporate use of MT. This paper describes our first step in system design: a hierarchical classification scheme of fluency errors in English MT output, to enable us to identify error types and frequencies, and guide the selection of errors for automated detection. We present results from the statistical analysis of 20,000 words of MT output, manually annotated using our classification scheme, and describe correlations between error frequencies and human scores for fluency and adequacy.</abstract>
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%0 Conference Proceedings
%T A fluency error categorization scheme to guide automated machine translation evaluation
%A Elliott, Debbie
%A Hartley, Anthony
%A Atwell, Eric
%Y Frederking, Robert E.
%Y Taylor, Kathryn B.
%S Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2004
%8 sep 28  oct 2
%I Springer
%C Washington, USA
%F elliott-etal-2004-fluency
%X Existing automated MT evaluation methods often require expert human translations. These are produced for every language pair evaluated and, due to this expense, subsequent evaluations tend to rely on the same texts, which do not necessarily reflect real MT use. In contrast, we are designing an automated MT evaluation system, intended for use by post-editors, purchasers and developers, that requires nothing but the raw MT output. Furthermore, our research is based on texts that reflect corporate use of MT. This paper describes our first step in system design: a hierarchical classification scheme of fluency errors in English MT output, to enable us to identify error types and frequencies, and guide the selection of errors for automated detection. We present results from the statistical analysis of 20,000 words of MT output, manually annotated using our classification scheme, and describe correlations between error frequencies and human scores for fluency and adequacy.
%U https://aclanthology.org/2004.amta-papers.8/
%P 64-73

Markdown (Informal)

[A fluency error categorization scheme to guide automated machine translation evaluation](https://aclanthology.org/2004.amta-papers.8/) (Elliott et al., AMTA 2004)

• A fluency error categorization scheme to guide automated machine translation evaluation (Elliott et al., AMTA 2004)

ACL

• Debbie Elliott, Anthony Hartley, and Eric Atwell. 2004. A fluency error categorization scheme to guide automated machine translation evaluation. In Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 64–73, Washington, USA. Springer.