基于改进注意力机制模型的智能英语翻译方法研究

doi:10.16180/j.cnki.issn1007-7820.2020.11.016

Abstract

Abstract:

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.

Key words: machine translation, attention mechanism, encoder, decoder, circulatory neural network, translation of long sentences, semantic connection, natural language processing

CLC Number:

TP391

ZHENG Meng. Research on Intelligent English Translation Method Based on Improved Attention Mechanism Model[J].Electronic Science and Technology, 2020, 33(11): 84-87.

Figures/Tables 8

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References 13

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参数	内容
数据语料大小	25 000
单词维度	512
神经网络层数	2
深度学习框架	TensorFlow
硬件配置	i7-9700K,128 GB

模型	BLUE值
RNN	15.88
LSTM	21.03
ATT+RNN	24.33

模型	BLUE值(L为单词个数)
模型	0≤L≤15	15<L≤30	L>30
RNN	16.56	15.02	12.39
LSTM	24.56	21.63	18.99
ATTRNN	25.32	24.99	22.33

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

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