ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Language and Computation
Volume 8 - 2025 | doi: 10.3389/frai.2025.1619489
A Multidimensional Comparison of ChatGPT, Google Translate, and DeepL in Chinese Tourism Texts Translation: Fidelity, Fluency, Cultural Sensitivity, and Persuasiveness
Provisionally accepted- 1Zhejiang Shuren University, Hangzhou, China
- 2ningxia normal university, Guyuan, China
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This study systematically compares the translation performance of ChatGPT, Google Translate, and DeepL on Chinese tourism texts, focusing on two prompt-engineering strategies. Using a mixedmethods approach that combines quantitative expert assessments with qualitative analysis, the evaluation centers on fidelity, fluency, cultural sensitivity, and persuasiveness. ChatGPT outperformed its counterparts across all metrics, especially when culturally tailored prompts were used. However, it occasionally introduced semantic shifts, highlighting a trade-off between accuracy and rhetorical adaptation. Despite its strong performance, human post-editing remains necessary to ensure semantic precision and professional standards. The study demonstrates ChatGPT's potential in domain-specific translation tasks while calling for continued oversight in culturally nuanced content.
Keywords: tourism, Tourism translation, Machine Translation, ChatGPT, prompt design Assessing ChatGPT's Translation Performances in Tourism Communication: A Comparative Analysis of Fidelity, fluency, Cultural sensitivity, and Persuasiveness
Received: 28 Apr 2025; Accepted: 14 Jul 2025.
Copyright: © 2025 Chen and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shiyue Chen, Zhejiang Shuren University, Hangzhou, China
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