CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor徐波
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword神经网络 机器翻译 模型压缩 解码加速 离线翻译
Other AbstractMachine translation (MT) realize the conversion between different natural language by computer, it has high application value. In recent years, Machine translation research has made considerable progress, and constantly improve the quality of translation, especially the rise of Neural Machine Translation (NMT), improved the fluency of translation greatly. However, neural machine translation also encountered some problems, restricting the further development of the practical application of machine translation. On the one hand, the neural machine translation model is deficient in the faithfulness of translation, and the result of translation often shows the phenomenon of omission and omission of phrases. In addition, the researchers have put forward a lot of translation performance optimization techniques for neural network machine translation, and the combination performance of these optimization techniques needs to be further verified. On the other hand, the computational complexity of the neural network machine translation model is large, which challenges the offline deployment of the model, especially the deployment on the mobile. The further optimization of neural machine translation technology is of great theoretical significance and practical value for improving the translation quality and practicability of machine translation. This paper first from the examination according to the characteristics of neural network model of the neural machine translation, based on the validation of a lot of work, through the integration of multiple techniques, enhance the practicability of neural machine translation.

Document Type学位论文
Recommended Citation
GB/T 7714
张晓伟. 基于神经网络的机器翻译关键技术研究[D]. 北京. 中国科学院研究生院,2018.
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