| Asia Pacific Journal of Corpus Research Vol. 7, No. 1, pp. 25-28 |
| Abbreviation: APJCR |
| e-ISSN: 2733-8096 |
| Publication date: 31 August 2026 |
| Received: 13 March 2026 / Received in Revised Form: 21 July 2026 / Accepted: 15 August 2026 |
| DOI: https://doi.org/10.22925/apjcr.2026.7.1.25
|
[Book Review] Machine Learning in Translation |
| Hao Yin (The Hong Kong Polytechnic University), CHINA; Jianwen Liu (Hong Kong Shue Yan University), CHINA |
| Copyright 2026 APJCR This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited. |
Abstract |
| This review examines Machine Learning in Translation, edited by Peng Wang and David B. Sawyer, as an accessible, human-centered introduction to the integration of machine learning into translation theory, practice, and education. Organized into three parts, the book connects human and machine approaches to translation, explains key technologies and tasks, including neural machine translation, quality assessment, and natural language processing, and highlights the central role of language data in translator–computer interaction. Particular attention is given to translators’ intentionality, model customization and personalization, machine learning literacy, and evolving professional competences. The book’s main strength lies in presenting machine learning as a continuation of linguistic, communicative, and cognitive approaches rather than merely an engineering domain. However, its predominantly conceptual orientation, limited empirical evidence, emphasis on resource-rich languages, and brief treatment of ethics and implementation restrict its scope. Overall, the volume provides a valuable roadmap for translation practitioners, educators, and researchers navigating increasingly data-driven, human–machine translation environments and future interdisciplinary collaboration. |
Keywords |
| Machine Learning, Translation Technology, Translator–Computer Interaction, Translator Education, Human-Centered Translation |
References |
| Bhattacharyya, P. (2015). Machine Translation. Boca Raton, FL: CRC Press.
Carleo, G., Cirac, I., Cranmer, K., Daudet, L., Schuld, M., Tishby, N., & Zdeborová, L. (2019). Machine learning and the physical sciences. Reviews of Modern Physics, 91(4), 045002. Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55. Koehn, P. (2009). Statistical Machine Translation. Cambridge: Cambridge University Press. Yang, S., Wang, Y., & Chu, X. (2020). A survey of deep learning techniques for neural machine translation. arXiv. https://arxiv.org/abs/2002.07526 |
The Author |
| Hao Yin (Anna) is a PhD student in the Department of Language Science and Technology at The Hong Kong Polytechnic University, Hong Kong, China.
Jianwen Liu (Kacey) is an Associate Professor in the Department of English Language and Literature at Hong Kong Shue Yan University, Hong Kong, China. |
The Author’s Address |
| First Author Hao Yin (Anna) PhD Student The Department of Language Science and Technology The Hong Kong Polytechnic University, Hong Kong, China Email: annayinhao@gmail.com Corresponding Author |
