Abstract The performance of statistical machine translation is improved by language model. However, the monolingual corpus is not equal to be effectively used by neural machine translation. To solve this problem, a semi-supervised neural machine translation model based on sentence-level bilingual evaluation understudy(BLEU) metric data selection is proposed. The candidate translations for non-labeled data are firstly generated by statistical machine translation and neural machine translation models, respectively. Then the candidate translations are selected through sentence-level BLEU, and the selected candidate translations are added to the labeled dataset to conduct semi-supervised joint training. The experimental results demonstrate the effectiveness of the proposed algorithm in the usage of non-labeled data. In the NIST Chinese-English translation tasks, the proposed method obtains an obvious improvement over the baseline system only with the fine labeled data.
Fund:Supported by National Key Research and Development Program of China(No.2016YFB1001303)
About author:: (YE Shaolin, born in 1993, master student. His research interests include machine translation.) (GUO Wu(Corresponding author), born in 1973, Ph.D., associate professor. His research interests include speech signal proce-ssing and natural language processing.)
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2017, Vol. 30 
