A study on the limitations of artificial intelligence translation tools in the rhetorical transmission of literary works—— Taking the English translation of Dream of the Red Chamber as an example
Abstract
This study examines the limitations of AI translation tools in conveying literary rhetoric through a case analysis of the English translation of *Dream of the Red Chamber*. The paper first highlights the rich rhetorical features in the novel and their significance in showcasing its literary value. It then analyzes challenges faced by AI translators when handling rhetorical devices such as puns, metaphors, and hyperbole, which often fail to accurately convey their nuanced meanings. By comparing machine-translated versions with human translations, the research reveals shortcomings in cultural context preservation and emotional expression subtlety. The study aims to draw attention to these limitations in literary translation applications, providing insights for enhancing translation quality and advancing translation technology development.
References
[1]Wang Weiwei, Wang Binhua. Current Status Survey on the Application of Artificial Intelligence-Assisted Machine Translation Tools in China’s Language Service Industry [J]. Foreign Language Audio-Visual Education, 2025,(02):25-30+100.DOI:10.20139/
j.issn.1001-5795.20250204.
[2]Ma Qian and Cui Xiaoci. Comparative Study on Translation Quality of AI-powered Translation Tools [J]. Kui Ge Journal, 2023,
No.2: 82-96.
[3]Gong Qian, Ma Linli, Guo Ting, et al. “Empowering English Major Students’ Online Translation Learning with Artificial Intelligence: Current Status and Strategies [J]. Computer Knowledge and Technology, 2021,17(12):186-187.DOI:10.14004/j.cnki.ckt. 2021.191.
[4]Ren Wen. Translation Practice and Translation Education in the Era of Generative Artificial Intelligence: From Instrumental Behavior to Communicative Behavior [J]. China Translation, 2024,45(06):48-57+192.
[5]Zhao Mengting. AI Empowering Innovative Teaching in College Tourism English Classrooms [J]. Tourism Review, 2025(11):82-84.

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