Statistical Phrase-Based Post-Editing

Abstract

We propose to use a statistical phrase-based machine translation system in a post-editing task: the system takes as in-put raw machine translation output (from a commercial rule-based MT system), and produces post-edited target-language text. We report on experiments that were per-formed 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 bet-ter 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 re-sults indicate that automatic post-editing constitutes a simple and efcient way of combining rule-based and statistical MT technologies. 1