The impact of CS structured computer-assisted translation system on English linguistics teaching in universities

In the age of information technology, the teacher is no longer the only source of information, and a single lecture mode can trigger a range of messages for learning emotions. For this reason, this paper designs a cs-structured computer-aided translation system composed of machine translation in a neural network. After solving the exposure bias problem, the model regularization method combined with the bi-directional decoding consistency is used to optimize the assisted translation system. Then, aspects of the effectiveness of students’ use of the system on their English proficiency improvement were studied. The test scores of the test and comparison classes showed that the mean values of the scores before and after the test of the test class were 13.58 and 15.94, with a mean difference of 2.36 points. The mean values of the scores before and after the test of the comparison class were 14.58 and 14.94, respectively, with a mean difference of 0.4. Comparing the absolute values of the mean differences between the scores before and after the test of the two classes, it is clear that the test class improved their English proficiency level significantly more than the comparison class.

Furthermore, the overall satisfaction of the teachers using the system reached 85.62%. Therefore, in terms of traditional teaching methods, the assisted translation system is more capable of improving students’ English proficiency. It enables teachers to improve English teaching efficiency in the classroom and promotes the modernization and intelligence of English teaching.