Multilingual Machine Translation System Based on Decoder Recurrent Neural Network

Abstract

In order to solve the problem that traditional machine translation cannot meet users’ needs due to its slow translation speed, an in-depth study on English translation model based on neural network was proposed. Firstly, three methods of model frame design, translation system design, and training frame design are studied. In order to improve the effectiveness and stability of the English translation model of recursive neural network, a model of English-Chinese machine translation system is designed. The system uses a knowledge-based context vector to map English and Chinese words, and uses a codec recursive neural network to achieve the results. After experiments and researches, the neural network can also efficiently deal with the long-distance reordering problem of multilanguage machine translation, which is difficult for statistical machine translation to deal with effectively. The neural network has opened a broad field of vision for machine translation research.