The Application of Intelligent Translation System Based on Machine Translation in English Education Curriculum Reforms

• Published in: Applied mathematics and nonlinear sciences Vol. 10; no. 1
• Main Author: Cao, Xiaoyan
• Format: Journal Article
• Language: English
• Published: Beirut Sciendo 01.01.2025; De Gruyter Poland
• Subjects: 97B20; Curricula; Dependent syntax; Education reform; English translation; Machine translation; Multi-head self-attention mechanism; Transformer model
• ISSN: 2444-8656; 2444-8656
• DOI: 10.2478/amns-2025-0562

Against the background of the increasingly prominent role of machine translation in the field of English translation, it has become a hot topic in the industry how to get help from it to promote the development of English teaching in English education reform. To this end, this paper proposes an intelligent translation model based on machine translation, which introduces syntactic structure information into the conventional Transformer model structure, combines with the multi-head self-attention mechanism, transforms the dependent syntactic tree into the form of relational matrix, and combines it with the original encoder in a weighted way to improve the translation performance. Based on the intelligent translation model, a Chinese-English bilingual intelligent translation system has been constructed to aid in the development of English teaching during the reform of English education curriculum. Taking the first-year business English majors of class 2023 of D Normal University as the research object, the English teaching experiment is carried out to analyze the practical effect of the application of the intelligent translation system of this paper in the reform of English education classes. The mean value of the post-test English total score of the students in the experimental class is 69.66, which is 2.61 higher than the mean value of the pre-test, and 3.14 higher than the post-test score of the control class, showing a significant difference (P=0.041<0.05). In the posttest questions, students in the experimental class scored higher than the control class in all types of questions and showed significant differences with the control class in translation questions, writing questions, and vocabulary and structure questions (P<0.05).