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

被引：0

作者：

Li Y. ^{[1
]}

机构：

[1] School of Aviation and Tourism Management, Chongqing Aerospace Polytechnic, Chongqing

来源：

Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期

关键词：

College English teaching; Computer-Aided translation system; CS structure; Machine translation; Neural network;

D O I：

10.2478/amns.2023.2.00114

中图分类号：

学科分类号：

摘要：

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. © 2023 Yanxia Li, published by Sciendo.

引用

共 30 条

[21]

Kalchbrenner N., Blunsom P., Recurrent continuous translation models, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, (2013)

[22]

Sutskever I., Vinyals O., Le Q.V., Sequence to sequence learning with neural networks, Advances in neural information processing systems, (2014)

[23]

Cho K., van Merrienboer B., Gulcehre C., Et al., Learning phrase representations using an encoder-decoder for statistical machine translation, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, (2014)

[24]

Bahdanau D., Cho K., Bengio Y., Neural machine translation by jointly learning to align and translate, (2014)

[25]

Wu Y., Schuster M., Chen Z., Et al., Google's neural machine translation system: Bridging the gap between human and machine translation, (2016)

[26]

Hassan H., Aue A., Chen C., Et al., Achieving human parity on automatic Chinese to English news translation, (2018)

[27]

Vaswani A., Shazeer N., Parmar N., Et al., Attention is all you need, Advances in Neural Information Processing Systems, (2017)

[28]

Shen S., Cheng Y., He Z., Et al., Minimum risk training for neural machine translation, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), (2016)

[29]

Siahbani M., Sarkar A., Lexicalized reordering for left-to-right hierarchical phrase-based translation, Conference of the European Chapter of the Association for Computational Linguistics, (2017)

[30]

Zheng X., Yao W., Zhang Y., Zhang X., Consistency regularization-based deep polynomial chaos neural network method for reliability analysis, Reliability Engineering and System Safety, (2022)

← 1 2 3 →