Keywords
Neural Machine Translation (NMT)
Large Language Models (LLMs)
ChatGPT
pilgrimage tourism terminology
Chinese-Uzbek translation
linguocultural features
cultural sensitivity
prompt engineering
tourism translation
AI-based translation models
religious tourism
How to Cite
Abstract
Artificial intelligence-based translation systems have emerged as a significant research object in contemporary linguistics and translation studies. The translation of pilgrimage tourism terminology between Chinese and Uzbek presents a particularly compelling case for investigation, given that religious and cultural terms in this domain carry distinctive linguocultural properties that conventional machine translation systems frequently fail to render adequately. Chen and Lin (2025) conducted a multidimensional comparative study evaluating the performance of ChatGPT, Google Translate, and DeepL in translating Chinese tourism texts into English. Drawing on their findings, it becomes possible to assess the capabilities and limitations of AI-based translation models when applied to specialized domains - most notably, pilgrimage tourism terminology, where cultural and religious specificity is paramount.References
1. Jiang, L., Wei, B., Al-Shaibani, M. (2025). Effective neural machine translation with human post-editing of Chinese intangible cultural heritage corpus into English. SAGE Open. https://doi.org/10.1177/21582440251386954
2. Shormani, M. Q., Alfahad, A. (2025). ChatGPT and the translation of religious academic texts. SAGE Open. https://doi.org/10.1177/21582440251343954
3. Vaxidova, F. S. (2024). Ingliz va o'zbek tillari ziyorat turizmi terminosistemasi birliklarining funksional-diskursiv shartlanishi [PhD dissertation, Buxoro davlat universiteti]. https://buxdu.uz/media/aftoreferat/vaxidova_fotima_saidovna2.pdf
4. Wang, X., Li, Z., Chen, H. (2023). Towards better Chinese-centric neural machine translation for low-resource languages. Engineering Applications of Artificial Intelligence. https://doi.org/10.1016/j.engappai.2023.106835
5. Yao, B., Jiang, M., Bobinac, T., Yang, D., Hu, J. (2024). Benchmarking machine translation with cultural awareness. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 13078-13096). Association for Computational Linguistics. https://arxiv.org/abs/2305.14328
6. Zaid, A., Bennoudi, H. (2023). AI vs human translators: A comparative study of religious text translation. International Journal of Linguistics, Literature and Translation, 6(8), 45-58. https://al- kindipublisher.com/index.php/ijllt/article/view/6384
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