Variational Translation Theory-Based Pedagogy for Machine Translation Post-Editing: A Case Study

Book: Variational Translation: Practical and Theoretical Explorations from India by CSMFL Publications

Shenghua Luo
School of Foreign Studies, Yangtze University, Jingzhou, China.
10.46679/9789349926769ch06
This chapter is a part of: Variational Translation: Practical and Theoretical Explorations from India
ISBN (Ebook):978-93-49926-76-9
ISBN (Hardcover Print):978-93-49926-38-7
ISBN (Softcover Print):978-93-49926-07-3

© CSMFL Publications & its authors.
Published: January 5, 2026

Luo, S. (2025). Variational Translation Theory-Based Pedagogy for Machine Translation Post-Editing: A Case Study. In T. Chuanmao, D. Juntao & Z. Huang, Variational Translation: Practical and Theoretical Explorations from India (pp 103-123). CSMFL Publications. https://dx.doi.org/10.46679/9789349926769ch06

Abstract

In the AI era, Machine Translation Post-editing has become the dominant workflow paradigm, yet there is still a lack of a systematic framework for cultivating post-editing competence in college English translation teaching. Based on Variational Translation Theory, the study proposes an innovative pedagogical model for machine translation post-editing in college English translation teaching. Addressing the current absence of systematic pedagogical frameworks, the model shifts from “error-correction-focused” to “variational translation-oriented,” prioritizing competence at syntactic and discourse levels. Through two controlled experiments with the 2023 cohort (N=117) utilizing neural machine translation and the 2024 cohort (N=181) employing large language models, the findings reveal: (1) altered translation (改译, gaiyi), edited translation (编译, bianyi), and annotated translation (阐译, chanyi) were the most frequently utilized; (2) AI-assisted instruction should shift its focus from lexical correction to syntactic restructuring and discourse optimization. The research has established Variational Translation Theory as a foundational framework for MTPE pedagogy, potentially transforming conventional practices in college English translation teaching through the theoretical perspectives and practical methodology.

Keywords: Variational Translation Theory, Machine Translation Post-Editing, Pedagogical Model, College English Translation Teaching

References

  1. Bassnett, S., & Lefevere, A. (2001) Constructing Cultures: Essays on Literary Translation. Shanghai: Shanghai Foreign Language Education Press.
  2. Carl, M., & Toledo Baez, M. C. (2019). Machine translation errors and the translation process: A study across different languages. The Journal of Specialised Translation, (31):107-132.
  3. Fan, Z.R., & Yang, W.D. (2024). Principles and Strategies of Post-editing in the Human-machine Coupling Era. Shanghai Journal of Translators, (4): 29-34.
  4. Feng, Q., & Cui, Q. (2016). Research Focuses and Trends in Post-editing of Machine Translation. Shanghai Journal of Translators, (6): 67-74+89+94.
  5. Geng, F., & Hu, J. (2023). New Direction for Post-Editing by Artificial Intelligence Translation: A Case Study of ChatGPT Translation. Foreign Languages in China, 20(3): 41-47.
  6. Huang, Z.L. (2002). Variational Translation Theory. Beijing: China Translation & Publishing Corporation.
  7. Huang, Z.L. (2012). A New Interpretation of “Translation”: A Response to Mr. Zhou Lingshun on “Variational Translation.” Foreign Language Research, (1): 81-84.
  8. Huang, Z.L., & Li, M.D. (2014). A Comparative Study on Translation Methods in Translation Variation. Foreign Language Research, 181(6): 88-91.
  9. Huang, Z.L., & Chen, Y.F. (2016). The Process from Adaptation Techniques to Translation Variation Theory. Foreign Languages and Their Teaching, (1): 98-106+149.
  10. UNESCO. (2009). Intangible Cultural Heritage of Humanity-Representative List of the Intangible Cultural Heritage of Humanity. Paris: UNESCO.
  11. ISO 18587: 2017- Translation Services – Post-editing of Machine Translation Output – Requirements. Available online: https://www.iso.org/standard/62970.html (accessed on 22 May 2025)
  12. Jia, Y.F. (2024). Integrating translation project management platforms with generative AI technologies: An investigation into the translation process through human-machine interaction. Foreign Language Teaching and Research, 56(6): 937-949+961.
  13. Koponen, M. (2016). Is Machine Translation Post-editing Worth the Effort? A Survey of Research into Post-editing and Effort. The Journal of Specialised Translation, (25): 131-148.
  14. Luo, S.H. (2025). Charting the Integration of Machine Translation Post-editing into College English Translation Teaching. Education Journal, 8(4): 236-248.
  15. Meng, F.Y., & Tang, X.R. (2020). Efficiency First: Reviewing Technologies of Machine Translation Post-Editing. Computer Engineering and Applications, 56(22): 25-32.
  16. Nitzke, J., Hansen-Schirra, S., & C. Canfora. (2019). Risk management and post-editing competence. The Journal of Specialised Translation, (31): 239-259.
  17. Qian. J.J., Xiao, W.Q., Li, Y., & Xiang, X. (2022) Impact of neural machine translation error types on translators’ allocation of attentional resources: Evidence from eye-movement data. Foreign Language Teaching and Research, 54(5): 750-761+800-801.
  18. Qiu, B.L., Wang, M.W., Li, M.X., Chen C., & Xu, F. (2022) Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation. Journal of Chinese Information Processing, 36(1): 47-55.
  19. Robert, I.S., Remael, A., & Ureel, J.J.J. (2017). Towards a model of translation revision competence. The Interpreter and Translator Trainer, 11(1): 1-19.
  20. Wang, G.Z. (2023). Re-interpretation of the Translation Right System in the Scene of artificial Intelligence Machine Translation. Journal of Political Science and Law, 3: 79-89.
  21. Wang, X.L., Li, X.Y., & Chen, G.J. (2024). Comparing translation revision and machine translation post-editing: Evidence from keylogging, retrospection and questionnaire. Foreign Language Learning Theory and Practice, 5: 88-97.
  22. Xia, L.; Li, Y.M., & Li, H.W. (2022) Machine translation and post editing of English abstracts in the context of artificial intelligence. Acta Editologica, 34(4): 396-401+406.
  23. Yang, Y.X., & Wei, X.Q. (2023). An analysis on machine translation post-editing competence and its cultivation from the perspective of categorization theory. Foreign Language Education, 44(1): 90-96.
  24. Yao, B. (2024). Exploration into Problems in Machine Translation and Post-Editing of United Nations Documents. Chinese Translators Journal, 45(04): 144-151.
  25. Zhong, W., & Shu, C. (2020). Competence Structure and Course Design of Post-Editing — A Frontier Analysis Based on Foreign Post-Editing Courses. Technology Enhanced Foreign Language Education, 6: 86-91+7.

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