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

Abstract

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.

Research on Multiple Teaching Models of Chinese Culture Integration in English Translation Course Based on Web Technology

“…English teachers in colleges and universities can make use of the network teaching platform to enrich the teaching resources of the courses, integrate and innovate, reform the traditional teaching methods, improve the teachers' cultural awareness and teaching ability, innovate the teaching evaluation mode, implement diversified teaching evaluation and other paths to integrate Chinese culture into the teaching of the English translation courses, and to improve the students' ability to tell Chinese stories [4][5][6]. To integrate Chinese culture into the English translation course and enhance the effect of the course's ideology and politics, the traditional university English teaching focuses on language knowledge [7][8].…”

“…In the context of Chinese culture "going global", the ideological and political teaching of foreign language courses, especially English translation courses, which integrates Chinese culture, has a positive role in strengthening students' cultural self-confidence, enhancing students' sense of identity and pride in Chinese culture, and improving students' ability to tell Chinese stories well, which is conducive to promoting the realization of the educational goals of English translation courses [4][5][6]. English teachers in colleges and universities can integrate Chinese culture into the teaching of English translation courses and enhance the effect of course ideology and politics by utilizing online teaching platforms to enrich course teaching resources, integrating innovation and reforming traditional teaching methods, enhancing teachers' cultural awareness and teaching ability, innovating course teaching evaluation modes, and implementing diversified teaching evaluations, among other paths [7][8].…”

Research on adaptive model of English translation based on data fusion

“…Similar to the process of human translation, the process of machine translation is divided into two stages: the first stage is to understand the meaning of the source language text (also known as the source text); In the second stage, based on the source language text, the target language text (also called translation) is generated according to the grammar and syntax of the target language. Since the pioneer of information theory, American scientist W. weaver and British engineer A.D [7][8]. booth proposed the idea of automatic language translation by computer in 1947, machine translation has experienced rule-based machine translation model and case-based machine translation model in the development history of more than 70 years, machine translation model based on statistics and machine translation model based on Neural Network type.…”

On the Principles and Decisions of New Word Translation in Sino-Japan Cross-Border e-Commerce: A Study in the Context of Cross-Cultural Communication

“…Based on the characteristics of multimedia, the research of multimodal machine translation integrating visual information of text and images has received attention from researchers at home and abroad in recent years. In literature [8], two independent attention mechanisms are used to process word and image regions in the source language separately for enhancing the translation results of the model. Literature [9] is one of the few papers that incorporate local visual features of images for multimodal machine translation.…”