Research on Mongolian-Chinese Machine Translation Based on Dual-Learning

The Deep Learning method sits on the field of machine translation by virtue of its ability to understand semantics, especially in the field of large languages. However, for low-resource languages, a difficult problem is the lack of large-scale bilingual corpus which leads to over-fitting of the model. In this paper, for languages with few data resources, Round-Trip Translation(RTT) is combined to expand the scale of pseudo-parallel corpus, while dual learning method is used to semi-supervised the model in two directions: source-target language and target-source language, then the updating of model parameters is guided by reward feedback. In addition, in order to reduce the "noise" effect of pseudo-corpus, an iterative attenuation method is proposed to refine the training data. Then, the CWMT2018 Mongolian-Chinese translation task is used to test the model. The results show that the BLEU value of the model is 2.1 higher than that of the traditional method, and the validity of the method is fully proved.