The voice of each language usually keeps different syntactic structure. In machine translation, it causes relatively low translation quality. To resolve this problem, an approach is proposed by integrating voice features into hierarchical phrase based (HPB) models. In the proposed method, corpus is firstly classified into three categories from Japanese side: passive voice, potential voice and others. Secondly, passive and potential sentences are classified into several groups according to the characteristics of English to build maximum entropy models for rules. Finally, bilingual voice features are integrated into log linear model for improving translation results and the accuracy of rule selection during the translation of passive and potential sentences. In Japanese to English translation task, large scale experiment shows that the proposed method can not only improve the problem of long distance reordering but also improve translation quality of both passive and potential voice test sets.

In consideration of the complexity and high cost of system construction in traditional examplebased machine translation (EBMT) methods, the authors propose a Chinese-English tree-to-string EBMT method. Compared with the traditional methods, the preposed approach just needed to implement the processing of source language parsing. Word segmentation, POS tagging and dependency parsing were jointed to relieve the affections of error propagation and failure of feature extraction at different levels. Moreover, the authors extracted and generalized bilingual word and phase alignments from examples and templates by using the dependency structure of source language. Experimental results show that the preposed method can achieve better performance significantly than baseline systems.