English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching

Abstract

The traditional Chinese-English translation model tends to translate some source words repeatedly, while mistakenly ignoring some words. Therefore, we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching. In this model, word vector, two-way LSTM, 2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs. Self-organizing mapping (SOM) is used to classify and identify the sentence feature. The attention mechanism-based neural machine translation model is taken as the baseline system. The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.