Statistical Phrase-based Post-editing

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TypeArticle
ConferenceHuman Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007), April 22-27, 2007., Rochester, New York, USA
AbstractWe propose to use a statistical phrase-based machine translation system in a post-editing task: the system takes as input raw machine translation output (from a commercial rule-based MT system), and produces post-edited target-language text. We report on experiments that were performed on data collected in precisely such a setting: pairs of raw MT output and their manually post-edited versions. In our evaluation, the output of our automatic post-editing (APE) system is not only better quality than the rule-based MT (both in terms of the BLEU and TER metrics), it is also better than the output of a state-of-the-art phrase-based MT System used in standalone translation mode. These results indicate that automatic post-editing constitutes a simple and efficient way of combining rule-based and statistical MT technologies.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number49288
NPARC number5764892
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Record identifieraaa8c0b3-9d7a-4db3-9b1d-c3deab67d09f
Record created2009-03-29
Record modified2016-05-09
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