AI-BASED TRANSLATION MODELS AND LINGUOCULTURAL FEATURE INTEGRATION IN CHINESE-UZBEK PILGRIMAGE TOURISM TERMINOLOGY
DOI:
https://doi.org/10.5281/zenodo.20266373Keywords:
Artificial Intelligence (AI), 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 discourse.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.
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