Information Design of Distance Teaching System for English Translation Course Based on Unsupervised Learning
- DOI
- 10.2991/978-94-6463-242-2_50How to use a DOI?
- Keywords
- Unsupervised learning; English; Translation course; Long-range; Teaching system; Promotion of information technology; Design
- Abstract
Remote learning systems are susceptible to feedback from business Slervlets, leading to abnormal operation of some functional modules. Therefore, it is necessary to design a new distance learning information system for English translation courses based on unsupervised learning. Designed FPGA chips, IDE memory controllers, and PIO transmitters. This paper constructs the remote teaching system architecture of English translation course, generates an English corpus translation engine based on unsupervised learning, designs the distance teaching information module of English translation course, and realizes the distance information teaching of translation course. The system testing results indicate that each functional module can operate in an orderly manner, with good performance, reliability, and certain application value.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jing Chen PY - 2023 DA - 2023/09/22 TI - Information Design of Distance Teaching System for English Translation Course Based on Unsupervised Learning BT - Proceedings of the 2023 4th International Conference on Artificial Intelligence and Education (ICAIE 2023) PB - Atlantis Press SP - 401 EP - 409 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-242-2_50 DO - 10.2991/978-94-6463-242-2_50 ID - Chen2023 ER -