Abstract:In the construction and training stage of the machine translation model, the maximum likelihood estimation principle used in the end-to-end machine translation framework training can lead to the low quality of the translation model. To alleviate the problem, this study uses the adversarial learning strategy to train generative adversarial networks and improves the translation quality of the generator with the assistance of discriminators. Through experiments, the machine translation framework, Transformer, is chosen for its better performance with generators, and the convolution neural network for its better performance with discriminators. The experimental results verify that adversarial training can improve the naturalness, fluency, and accuracy of the translation. In the model optimization stage, the Mongolian-Chinese machine translation quality is still unsatisfactory due to the lack of Mongolian-Chinese parallel data sets. For improvement, the dual-generative adversarial networks (Dual-GAN) algorithm is introduced to the Mongolian-Chinese machine translation. Through the effective use of a large number of Mongolian-Chinese monolingual data, the dual learning strategy is adopted to further improve the quality of the Mongolian-Chinese machine translation model based on adversarial learning.