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Machine translation of Chinese classical poetry: a comparison among ChatGPT, Google Translate, and DeepL Translator

Author

Listed:
  • Ruiyao Gao

    (The Chinese University of Hong KongKong)

  • Yumeng Lin

    (The Chinese University of Hong KongKong)

  • Nan Zhao

    (Hong Kong Baptist University)

  • Zhenguang G. Cai

    (The Chinese University of Hong KongKong
    The Chinese University of Hong Kong)

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.

Suggested Citation

  • Ruiyao Gao & Yumeng Lin & Nan Zhao & Zhenguang G. Cai, 2024. "Machine translation of Chinese classical poetry: a comparison among ChatGPT, Google Translate, and DeepL Translator," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03363-0
    DOI: 10.1057/s41599-024-03363-0
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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