Design of a Smart Teaching English Translation System Based on Big Data Machine Learning

被引：0
作者：
Zhang C. [1 ]
Yu T. [1 ]
Gao Y. [1 ]
Tham M.L. [2 ]
机构：
[1] Harbin University of Science and Technology, China
[2] Universiti Tunku Abdul, Malaysia
来源：
International Journal of Web-Based Learning and Teaching Technologies | 2023年 / 18卷 / 02期
关键词：
Artificial Intelligence; English Translation; Intelligent Robot; Machine Learning; Smart Teaching;
D O I：
10.4018/IJWLTT.330144
中图分类号：
学科分类号：
摘要：
In the context of artificial intelligence, the use of machine translation in English reading classroom teaching is a more common learning method. In traditional teaching methods, machine translation is more convenient and faster than human translation, but it often deviates from the original text in terms of grammar and sentence pattern. Based on the perspective of English reading class, this paper compares traditional and machine translation, and discusses the future development trend and influence mechanism of the current situation of using machine translation in English reading class under the effect of artificial intelligence. © 2023 International Journal of Social Ecology and Sustainable Development. All rights reserved.
引用
收藏
相关论文
共 29 条
  • [1] Arumugam K., Kumar L. A., Design and development of intelligent home rehab device for post stroke therapy with electro-hydraulic interventions, Acupuncture & Electro-Therapeutics Research, 47, 1, pp. 1-8, (2021)
  • [2] Bahdanau D., Cho K., Bengio Y., Neural machine translation by jointly learning to align and translate, (2014)
  • [3] Bondaryk L. G., Hsi S., Van Doren S., Probeware for the modern era: IoT dataflow system design for secondary classrooms, IEEE Transactions on Learning Technologies, 14, 2, pp. 226-237, (2021)
  • [4] Chen D., Cheng P., Development of design system for product pattern design based on Kansei engineering and BP neural network, International Journal of Clothing Science and Technology, 34, 3, pp. 335-346, (2022)
  • [5] Church A., Lloyd J., Hadsell R., Lepora N. F., Optical tactile sim-to-real policy transfer via real-tosim tactile image translation, Proceedings of the 5th Conference on Robot Learning, PMLR, 164, pp. 1645-1654, (2021)
  • [6] Dammer G., Bauer H., Neumann R., Major Z., Design additive manufacturing and component testing of pneumatic rotary vane actuators for lightweight robots, Rapid Prototyping Journal, 28, 11, pp. 20-32, (2022)
  • [7] Doltsinis S., Krestenitis M., Doulgeri Z. A., Machine learning framework for real-time identification of successful snap-fit assemblies, IEEE Transactions on Automation Science and Engineering, 17, 1, pp. 513-523, (2020)
  • [8] Eludiora S., Ajibade B., Design and implementation of English to Yoruba verb phrase machine translation system, African NLP Workshop, 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL), (2021)
  • [9] Green K., Smit-Anseeuw N., Gleason R., Remy C. D., Design and control of a recovery system for legged robots, 2016 International Conference on Advanced Intelligent Mechatronics (AIM), pp. 958-963, (2016)
  • [10] Grols J. R., Castro-Dominguez B., Intelligent mechanochemical design of co-amorphous mixtures, Crystal Growth & Design, 22, 5, pp. 2989-2996, (2022)
123