Statistical Post-Editing of a Rule-Based Machine Translation System
نویسندگان
چکیده
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 postedited with an increase in productivity. In this work, we present an APE system that uses statistical models to enhance a commercial rulebased 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.
منابع مشابه
Using Statistical Post-editing to Improve the Output of Rule-based Machine Translation System
USING STATISTICAL POST-EDITING TO IMPROVE THE OUTPUT OF RULE-BASED MACHINE TRANSLATION SYSTEM
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