title: Statistical Phrase-based Post-editing
creator: Simard, Michel
creator: Goutte, Cyril
creator: Isabelle, Pierre
subject: Computational Linguistics
subject: Machine Learning
subject: Artificial Intelligence
description: We 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. 
date: 2007
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/5627/1/N07-1064.pdf
identifier:   Simard, Michel and Goutte, Cyril and Isabelle, Pierre  (2007) Statistical Phrase-based Post-editing.  [Conference Paper]     
relation: http://cogprints.org/5627/