Abstract
Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.
Highlights
In recent years, with the continuous development of education and science and technology, the number of machine translation application products is increasing
The English translation model based on intelligent recognition and deep learning designed in this paper, the intelligent recognition English translation model based on the improved GLR algorithm designed in Reference [6], and the syntax-based neural machine English translation model designed in Reference [7] are used to test the English translation of the above samples
In order to verify the effectiveness of this model, the English translation model based on intelligent recognition and deep learning designed in this paper, the intelligent recognition English translation model based on the improved GLR algorithm designed in Reference [6], and the neural machine English translation model based on syntax designed in Reference [7] are used to identify the part of speech of English phrases and verify the accuracy of the three models
Summary
With the continuous development of education and science and technology, the number of machine translation application products is increasing. Thanks to the development of big data, many researchers seek to complete translation through computer-aided translation (CAT) [4], so as to improve the accuracy of English translation and achieve real-time and accurate English translation They build English translation models to promote the further development of English machine translation technology. Reference [6] designs an intelligent recognition English translation model based on the improved GLR algorithm. Reference [7] designs a neural machine English translation model based on syntax. This paper designs an English translation model by combining intelligent recognition and deep learning, in order to alleviate the disadvantages of structural ambiguity in the current field of English translation, improve the efficiency of phrase recognition, and enhance the quality of English translation. 2. Design of English Translation Model Based on Intelligent Recognition and Deep Learning
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