基于改进注意力机制模型的智能英语翻译方法研究  

Research on Intelligent English Translation Method Based on Improved Attention Mechanism Model

作　　者：郑萌 ZHENG Meng(Dalian Neusoft University of Information,Dalian 116023,China)

机构地区：[1]大连东软信息学院,辽宁大连116023

出　　处：《电子科技》2020年第11期84-87,共4页Electronic Science and Technology

基　　金：辽宁省高等教育学会“十二五”教学改革专项(WYYB150185)。

摘　　要：使用神经机器算法对英语进行翻译是当前研究的热点,采用传统序列神经框架进行英语翻译,其对长距离信息的捕获能力过差,自身有较大的局限性。然而,目前的改进框架,例如循环神经网络翻译效果也并不理想。文中针对传统机器翻译算法的不足,建立了注意力编解码模型,将注意力机制与神经网络框架相结合,并基于TensorFlow对整个英语翻译系统进行实现,由此提高了翻译精度。实验测试结果表明,文中所构建算法模型的BLUE值相比于传统机器学习算法均有不同程度的提升,证明了文中所提算法模型的性能相较于传统模型有较为明显的提高。The use of neural machine algorithms to translate English is a hot topic in current research.The ability to capture long-distance information in English translation using traditional sequence neural frameworks is too poor and has its own limitations.The current improved frameworks,such as recurrent neural network translation effect is not ideal.Aiming at the shortcomings of traditional machine translation algorithms,this paper establishes an attention coding and decoding model,combines the attention mechanism with a neural network framework,and uses TensorFlow to implement the entire English translation system.This method can improve the accuracy of translation.Experiments show that the BLUE value of the algorithm model constructed in the article has different degrees of improvement compared with traditional machine learning algorithms,which further proves the effectiveness of the improved attention mechanism model proposed in this article in English translation.

关 键 词：机器翻译 注意力机制 编码器 解码器 循环神经网络 长句翻译 语义联系 自然语言处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]