AEPH
Home > Higher Education and Practice > Vol. 2 No. 1 (HEP 2025) >
Application of Artificial Intelligence in Computer-aided Translation
DOI: https://doi.org/10.62381/H251112
Author(s)
Juan Pan
Affiliation(s)
Zhengzhou University of Science and Technology, Zhengzhou, Henan, China
Abstract
In the contemporary era marked by the rapid advancement of modern science and technology, particularly computer technology, artificial intelligence has increasingly converged with numerous traditional scientific disciplines. This integration has given rise to computer-aided translation technology, which has dramatically enhanced translation efficiency and slashed translation costs, thereby gaining widespread favor across various industries. This paper delves into the development trend of computer-aided translation, meticulously details its process, and comprehensively summarizes its merits and demerits. The aim is to offer valuable references for researchers in the field of computer-aided translation. Moreover, the exploration of computer-aided translation within the context of artificial intelligence sheds light on interdisciplinary research, providing insights that could benefit multiple domains.
Keywords
Computer-Aided Translation; AI; Machine Translation; NMT; NLP
References
[1] Zhang Wei, Li Ming. Advances in Machine Translation Technology. Journal of Computational Linguistics, 2021, 25(2): 145-160. [2] Johnson, M. & Koehn, P. A Brief Survey of Machine Translation. Computational Linguistics, 2024, 45(1): 1-20. [3] Wu, Y. & Zhang, H. Neural Machine Translation: A Comprehensive Survey. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(4): 1234-1245. [4] Brown, P. F. The Mathematics of Statistical Machine Translation. Computational Linguistics, 1990, 16(2): 263-311. [5] Sennrich, R. Neural Machine Translation of Rare Words with Subword Units. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016. [6] Vaswani, A. Attention is All You Need. Advances in Neural Information Processing Systems, 2025, 22: 78-82 [7] Edunov, S. Understanding Back-Translation for Neural Machine Translation. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018. [8] Freitag, M. Fast and Flexible Decoding for Neural Machine Translation. Proceedings of the 2017 Conference on Machine Translation, 2017. [9] Gu, J. Incorporating Copying Mechanism in Sequence-to-Sequence Learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016. [10] Cheng, Y. Semi-Supervised Learning for Neural Machine Translation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.
Copyright @ 2020-2035 Academic Education Publishing House All Rights Reserved