RETRACTED: Research on Intelligent English Translation Method Based on the Improved Attention Mechanism Model (Retracted Article)

被引:1
作者
Wang, Rong [1 ]
机构
[1] Shaanxi Xueqian Normal Univ, Sch Foreign Languages, Xian 710100, Shaanxi, Peoples R China
关键词
PREDICTION;
D O I
10.1155/2021/9667255
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The use of neural machine algorithms for English translation is a hot topic in the current research. English translation using the traditional sequential neural framework, which is too poor at capturing long-distance information, has its own major limitations. However, the current improved frameworks, such as recurrent neural network translation, are not satisfactory either. In this paper, we establish an attention coding and decoding model to address the shortcomings of traditional machine translation algorithms, combine the attention mechanism with a neural network framework, and implement the whole English translation system based on TensorFlow, thus improving the translation accuracy. The experimental test results show that the BLUE values of the algorithm model built in this paper are improved to different degrees compared with the traditional machine learning algorithms, which proves that the performance of the proposed algorithm model is significantly improved compared with the traditional model.
引用
收藏
页数:8
相关论文
共 34 条
[31]   Positioning optimisation based on particle quality prediction in wireless sensor networks [J].
Zhang, Chunjiong ;
Xie, Tao ;
Yang, Kai ;
Ma, Hui ;
Xie, Yuxia ;
Xu, Yueyao ;
Luo, Pan .
IET NETWORKS, 2019, 8 (02) :107-113
[32]   Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs [J].
Zhang, Zhengwan ;
Zhang, Chunjiong ;
Li, Mingyong ;
Xie, Tao .
IEEE ACCESS, 2020, 8 :127709-127719
[33]  
Zhao S., 2018, P 32 AAAI C ARTIFICI
[34]  
Zhiyuan Zhang, 2019, Natural Language Processing and Chinese Computing. 8th CCF International Conference, NLPCC 2019. Proceedings. Lecture Notes in Artificial Intelligence, Subseries of Lecture Notes in Computer Science (LNAI 11839), P157, DOI 10.1007/978-3-030-32236-6_13