We propose to use a statistical phrasebased 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 stateof-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.
In an early work, REF ) combined a rule-based MT (RBMT) with a statistical MT (SMT) for monolingual post-editing.
Statistical Phrase-Based Post-Editing
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference
Computer Science