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

作　　者：Shu Ma 

机构地区：[1]College of International Business,Shenyang Normal University No.253 Huanghe North Street,Shenyang 110034,China

出　　处：《IJLAI Transactions on Science and Engineering》2024年第3期1-8,共8页IJLAI科学与工程学报汇刊（英文）

摘　　要：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.

关 键 词：Chinese-English translation model Self-organizing mapping neural network Deep feature matching Deep learning 

分 类 号：H31[语言文字—英语]