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Statistical Phrase-based Post-editing

By Michel Simard, Cyril Goutte and Pierre Isabelle


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

Topics: Computational Linguistics, Machine Learning, Artificial Intelligence
Year: 2007
OAI identifier:

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