Abstract：The suffixes of Japanese predicates have complex formation of different voice. Both passive and potential predicates are formed with the same suffix which originated from the same stem, which cause mistranslation in statistical machine translation. In this paper, a new method has been proposed for rule selection among different voice. Maximum entropy models are built to effectively classify passive and potential voice, and then voice features are integrated into the log-linear model translation model. In Japanese to Chinese translation task, large scale experiment shows that our approach improves the translation performance from 41.50 to 42.01 in BLEU score, and the informativness is 2.71% higher according to the human evaluation results.
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2016, Vol. 30 
