Knowledge Transfer in the Teaching of English Translation Based on Deep Learning

Authors

  • Xinyi Cheng College of English Language and Culture, Xi’an FanYi University, Xi’an 710105, China

Abstract

This study was conducted to explore the utilization of deep learning concepts and techniques in the education domain as a means of improving the effectiveness of English translation teaching and the transfer and application of students’ translation knowledge and skills. The model-building process consists of three stages: the pre-training stage, the fine-tuning stage and the translation stage. The pre-training stage involves deep learning on a large-scale teaching dialog or text corpus; the fine-tuning stage involves knowledge transfer on a relatively small-scale real-world corpus; and the translation stage involves the translation of new real-world texts. This study collected and analyzed data collected by means of an experiment designed and implemented A deep-learning-based English translation teaching experiment was designed and implemented to compare three teaching models (a traditional model, a guided classroom model, and a transfer learning model) in terms of students’ translation competence, deep learning competence, and learning satisfaction. The results of the study suggest that the knowledge transfer model incorporating deep learning was the most effective, the guided classroom model was the second most effective, and the traditional model was the least effective.

Keywords: deep learning, English translation, pedagogical knowledge transfer

Cite As

X. Cheng, "Knowledge Transfer in the Teaching of English Translation Based on Deep Learning",
Engineering Intelligent Systems, vol. 32 no. 6, pp. 597-604, 2024.






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Published

2024-11-01

Issue

Vol. 32 No. 6 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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