The creativity and limitations of AI neural machine translation
A corpus-based study of DeepL’s English-to-Chinese translation of Shakespeare’s plays
This study examines the performance of the neural machine translation system DeepL in translating Shakespeare’s
plays Coriolanus and The Merchant of Venice. The aim here is to explore the strengths and
limitations of an AI-based English-Chinese translation of literary texts. Adopting a corpus-based approach, the study investigates
the accuracy and fluency rates, the linguistic features, and the use of various methods of translation in the Chinese translations
of Shakespeare’s plays conducted via DeepL. It compares these to the translations by Liang Shiqiu, a well-known Chinese
translator. The study finds that DeepL performs well in translating these works, with an accuracy and fluency rate of above 80% in
sampled texts, showing the potential of the use of neural machine translation in translating literary texts across distant
languages. Our research further reveals that the DeepL translations exhibit a certain degree of creativity in their use of
translation methods such as addition, explicitation, conversion and shift of perspective, and in the use of Chinese sentence-final
modal particles, as well as Chinese modal verbs. On the other hand, the system appears to be limited in that a certain amount of
translation errors are present, including literal translations.
Article outline
- 1.Introduction
- 2.MT in the translation of literary texts
- 3.Research design
- 3.1The corpus for the research
- 3.2Research procedures
- 3.3Theoretical framework
- 3.3.1Sentence-final modal particles
- 3.3.2Modal verbs
- 3.3.3Translation methods
- 4.Results and discussion
- 4.1Errors in the Chinese translations of Shakespeare’s Plays by DeepL
- 4.2Linguistic features of the Chinese translations by DeepL
- 4.2.1The use of Chinese sentence-final modal particles
- 4.2.2The use of Chinese modal verbs
- 4.3The use of translation methods in the translations by DeepL
- 4.3.1Explicitation
- 4.3.2Conversion
- 4.3.3Shift of perspective
- 5.Conclusion
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References