Abstract

Recent studies have highlighted ChatGPT’s remarkable capabilities in machine translation. However, little attention has been paid to its application in literary translation, particularly within the realm of Chinese classical poetry. To explore the potential of ChatGPT’s abilities in poetry translation, we conducted a comparative analysis of poetry translation quality, contrasting ChatGPT (with two different prompts) with Google Translate and DeepL Translator regarding fidelity, fluency, language style, and machine translation style. The results revealed that ChatGPT outperformed Google Translate and DeepL Translator in all evaluation criteria, suggesting its exceptional ability in poetry translation. Furthermore, when employing a prompt that instructs ChatGPT to preserve the rhythm and rhyme of poems, ChatGPT demonstrated a remarkable ability to retain the beauty of the original poetic language, setting itself apart from conventional machine translation systems. Our analysis further elucidated ChatGPT’s proficiency in comprehending and translating some common symbols, imagery, and underlined semantic components which contributes to coherent and fluent translations. Our research opens up ChatGPT’s new possibilities in translating ancient literary texts into foreign languages.

Details

Title
Machine translation of Chinese classical poetry: a comparison among ChatGPT, Google Translate, and DeepL Translator
Author
Gao, Ruiyao 1 ; Lin, Yumeng 1 ; Zhao, Nan 2 ; Cai, Zhenguang G. 3   VIAFID ORCID Logo 

1  The Chinese University of Hong KongKong, Department of Linguistics and Modern Languages, Shatin, Hong Kong SAR (GRID:grid.10784.3a) (ISNI:0000 0004 1937 0482) 
2  Hong Kong Baptist University, Department of Translation, Interpreting and Intercultural Studies, Kowloon, Hong Kong SAR (GRID:grid.221309.b) (ISNI:0000 0004 1764 5980) 
3  The Chinese University of Hong KongKong, Department of Linguistics and Modern Languages, Shatin, Hong Kong SAR (GRID:grid.10784.3a) (ISNI:0000 0004 1937 0482); The Chinese University of Hong Kong, Brain and Mind Institute, Shatin, Hong Kong SAR (GRID:grid.10784.3a) (ISNI:0000 0004 1937 0482) 
Pages
835
Publication year
2024
Publication date
Dec 2024
Publisher
Springer Nature B.V.
e-ISSN
2662-9992
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1057/s41599-024-03363-0
ProQuest document ID
3072393444
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.