Abstract
AbstractRecent 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.
Publisher
Springer Science and Business Media LLC
Reference56 articles.
1. Ali MA (2020) Quality and machine translation: an evaluation of online machine translation of English into Arabic texts. Open J Modern Linguist 10(5):524–548
2. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
3. Bang Y, Cahyawijaya S, Lee N, Dai W, Su D, Wilie B, Fung P (2023) A multitask, multilingual, multimodal evaluation of ChatGPT on reasoning, hallucination, and interactivity. arXiv preprint arXiv:2302.04023
4. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67(1):1–48. https://doi.org/10.18637/jss.v067.i01
5. Bawden R, Yvon F (2023) Investigating the translation performance of a large multilingual language model: the case of bloom. arXiv preprint arXiv:2303.01911
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