ArticleTranslation Theories and Skills http://dx.doi.org/10.26855/tfll.2025.12.016
A Comparative Study of Translation Features of Cultural-loaded Terms in the 2025 Government Work Report—A Corpus-based Analysis of the Official Translation and AI Translation
Yiling Hu, Mengjia Peng*
School of International Communication, Wuhan Business University, Wuhan 430056, Hubei, China.
*Corresponding author: Mengjia Peng
Published: December 31, 2025
Abstract
This study constructs the Chinese-English Parallel Corpus of Cultural-Loaded Terms in the 2025 Government Work Report, abbreviated as 2025GWR-CLPC. From the corpus, 86 cultural-loaded terms are selected and divided into three categories, namely the policy and philosophy category, the system and mechanism category, and the livelihood and development category. Based on the Skopos Theory, this study adopts many methods including parallel corpus com-parison, quantitative and qualitative analysis, and audience comprehension assessment to compare the translation features of the above-mentioned terms in the official English version and the Doubao AI English version. The research results show that literal translation and free translation are the main strategies adopted by the two different versions. Official translation fits professional groups and formal occasions better, while AI translation is more ideal for the general public and quick information transmission. For improving the translation effect of culture-loaded expressions in political texts, it is necessary to combine the strengths of the two translation types, set up a standardized term base, and emphasize audience-focused translation.
Keyword
Cultural-loaded terms; Government Work Report; AI translation
References
Baker, M. (2020). Corpus-based translation studies: An introduction (2nd ed.). Routledge.
https://doi.org/10.4324/9780429276904
Hall, E. T. (2018). The silent language. Anchor Books. https://doi.org/10.1002/9781118900211
Li, J., & Wang, Y. (2023). A comparative study of AI translation and human translation in Chinese political text translation. Journal of Translation Studies, 18(3), 56-72. https://doi.org/10.15986/j.issn.1672-9358.2023.03.005
Liu, Y. (2024). Research on the application and optimization of large language model-based AI translation in political text translation. Modern Foreign Languages, 47(1), 120-128. https://doi.org/10.13766/j.bjmu.2024.0012
Reiss, K., & Vermeer, H. J. (2013). Towards a general theory of translational action. Routledge.
https://doi.org/10.4324/9781315873983
Wang, Z. (2020). The application of skopos theory in the translation of Chinese government documents. Journal of Foreign Languages, 43(4), 98-105. https://doi.org/10.13508/j.cnki.jfys.2020.04.010
Zhang, H. (2019). A study on the translation strategies of cultural-loaded terms in Chinese Government Work Reports (2010-2019). Chinese Translators Journal, 40(2), 67-74. https://doi.org/10.13508/j.cnki.cn11-2772/h.2019.02.009
Zhang, H., & Liu, J. (2021). Corpus-based analysis of the translation features of cultural-loaded terms in recent Government Work Reports. Shanghai Translation, (3), 45-51+98. https://doi.org/10.16027/j.cnki.shyj.2021.03.007
Copyright
© 2025 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
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How to cite this paper
A Comparative Study of Translation Features of Cultural-loaded Terms in the 2025 Government Work Report—A Corpus-based Analysis of the Official Translation and AI Translation
How to cite this paper: Yiling Hu, Mengjia Peng. (2025). A Comparative Study of Translation Features of Cultural-loaded Terms in the 2025 Government Work Report—A Corpus-based Analysis of the Official Translation and AI Translation. Translation and Foreign Language Learning, 1(5), 845-849.
DOI: http://dx.doi.org/10.26855/tfll.2025.12.016