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Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Focused on the issue of semantic independence in Chinese-Uyghur statistical machine translation system, this paper proposed a bilingual relatedness optimization model based on neural machine translation method. The model utilized the attention mechanism to capture word alignment information as well as introduced bilingual phrase semantic relevance and inner word correlation to predict the conditional probability of bilingual phrase pair. And it took the probability as bilingual relatedness to optimize the phrase translation scores in statistical translation model. Experimental results on the 11th China Workshop on Machine Translation ( CWMT 2015 ) Chinese-Uyghur public machine translation datasets show that the proposed approach can achieve obvious improvements both in the phrase-level and the sentence-level machine translation tasks, which outperforms the baseline system with a relative small-scale training data and vocabulary. The highest BLEU point of the proposed algorithm gains 2. 49 and 0. 59 respectively. [ABSTRACT FROM AUTHOR]