English Translation Teaching Algorithm in Colleges and Universities Using Data-Driven Deep Learning

Abstract

The data-driven learning model is a newer learning method. It not only provides learners with rich, diverse, and real-language big data, but it also creates an ideal learning environment for them because of its corpus-based teaching and learning characteristics. This paper will look at how to analyze and research data-driven learning in college English translation classes, as well as describe the data-driven approach. This paper presents the problem of data-driven translation teaching design, then expands on the concepts and learning methods of deep learning, and conducts case design and analysis of college English translation teaching. The results of the experiments show that there are numerous issues in traditional translation instruction, some of which are caused by students and others by teachers. The overall situation of the experimental group is better than that of the control group after the application of the college English translation teaching design, indicating that the DDL method is better than the traditional method ( Mean = 5.02 > 0 ), and the overall effect of DDL is better than that of the traditional method.