Statistical post-editing of a rule-based machine translation system

Abstract

Automatic post-editing (APE) systems aim at correcting the output of machine translation systems to produce better quality translations, i.e. produce translations can be manually post-edited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rule-based machine translation (RBMT) system. In addition, a procedure for effortless human evaluation has been established. We have tested the APE system with two corpora of different complexity. For the Parliament corpus, we show that the APE system significantly complements and improves the RBMT system. Results for the Protocols corpus, although less conclusive, are promising as well. Finally, several possible sources of errors have been identified which will help develop future system enhancements.