基于对抗学习的蒙汉神经机器翻译

在机器翻译模型的构建和训练阶段,为了缓解因端到端机器翻译框架在训练时采用最大似然估计原理导致的翻译模型的质量不高的问题,本文使用对抗学习策略训练生成对抗网络,通过鉴别器协助生成器的方式来提高生成器的翻译质量,通过实验选择出了更适合生成器的机器翻译框架Transformer,更适合鉴别器的卷积神经网络,并且验证了对抗式训练对提高译文的自然度、流利度以及准确性都具有一定的作用.在模型的优化阶段,为了缓解因蒙汉平行数据集匮乏导致的蒙汉机器翻译质量仍然不理想的问题,本文将Dual-GAN (dual-generative adversarial networks,对偶生成对抗网络)算法引入了蒙汉机器翻译中,通过有效的利用大量蒙汉单语数据使用对偶学习策略的方式来进一步提高基于对抗学习的蒙汉机器翻译模型的质量.

Mongolian-Chinese Neural Machine Translation Based on Adversarial Learning

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 paper 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, was 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.