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
-
References
References (14)
References
Al-Batineh, Mohammed, and Reem Ibrahim Rabadi. 2019. “Will
the Machine Understand Literary Translation? A Glimpse of the Future of Literary Machine Translation through the Lenses of
Artificial Intelligence.” Studies in
Translation 5 (1): 151–169.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Besacier, Laurent, and Lane Schwartz. 2015. “Automated
Translation of A Literary Work: A Pilot Study.” In Proceedings of
NAACL-HLT Fourth Workshop on Computational Linguistics for
Literature, 114–122. Denver, CO: Association for Computational Linguistics. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Halliday, Michael, and Alexander Kirkwood. 2000. An
Introduction to Functional Grammar. Beijing: Foreign Language Teaching and Research Press.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Hu, Kaibao 胡开宝. 2015. Jiyu yuliaoku de Shashibiya xiju hanyi1yanjiu 基于语料库的莎士比亚戏剧汉译研究 [A corpus-based study of the Chinese translations of
Shakespeare’s plays]. Shanghai: Shanghai jiaotong daxue chubanshe.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Jones, Ruth, and Ann Irvine. 2013. “The
(Un)Faithful Machine Translator.” In Proceedings of the 7th Workshop
on Language Technology for Cultural Heritage, Social Sciences, and Humanities, edited
by Piroska Lendvai and Kalliopi Zervanou, 96–101. Sofia: Association for Computational Linguistics.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Liang, Shiqiu 梁实秋. 1968. “Guanyu
Shashibiya de fanyi” 关于莎士比亚的翻译 [On the translation of Shakespeare’s plays]. In Shashibiya danchen sibai zhounian jinian ji 莎士比亚诞辰四百周年纪念集 [An anthology in honor of the 400th anniversary of Shakespere’s
birth], edited by Shiqiu Liang, 5621. Taipei: Guoli bianyi guan.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Matusov, Evgeny. 2019. “The Challenges of Using Neural Machine Translation for Literature.” In Proceedings of the Qualities of Literary Machine
Translation, edited by James Hadley, Maja Popović, Haithem Afli, and Andy Way, 10–19. Dublin: European Association for Machine Translation.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Palmer, Frank Robert. 1990. Modality and the English
Modals. London: Longman Group Limited.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Vanmassenhove, Eva, Dimitar Shterionov, and Andy Way. 2019. “Lost
in Translation: Loss and Decay of Linguistic Richness in Machine
Translation.” In Proceedings of Machine Translation Summit XVII
Volume 1: Research Track, edited by Mikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 222–232. Dublin: European Association for Machine Translation.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Van Brussel, Laura, Arda Tezcan, and Lieve Macken. 2018. “A
Fine-grained Error Analysis of NMT, PBMT and RBMT Output for English-to
Dutch.” In Proceedings of the Eleventh International Conference on
Language Resources and Evaluation, edited by Nicoletta Calzolari et al., 3799–3844. Miyazaki: European Language Resources Association.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Voigt, Rob, and Dan Jurafsky. 2012. “Towards
a Literary Machine Translation: The Role of Referential
Cohesion.” In The 2012 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), edited
by Eric Fosler-Lussier, Ellen Riloff, and Srinivas Bangalore, 18–25. Montréal: Association for Computational Linguistics.![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Webster, Rebecca, Margot Fonteyne, Arda Tezcan, Lieve Macken, and Joke Daems. 2020. “Gutenberg
Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English
Literary
Classics.” Informatics 7 (3): 32. ![DOI logo](https://benjamins.com/logos/doi-logo.svg)
![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Yang, Liu, and Min Zhang. 2015. “Statistical
Machine Translation.” In The Routledge Encyclopedia of Translation
Technology, edited by Chan Sin-wai, 201–213. New York: Routledge![Google Scholar](https://benjamins.com/logos/google-scholar.svg)
Cited by (2)
Cited by two other publications
Ali, Danish, Sundas Iqbal, Shahid Mehmood, Irshad Khalil, Inam Ullah, Habib Khan & Farhad Ali
2025.
Unleashing the Power of AI in Communication Technology: Advances, Challenges, and Collaborative Prospects. In
Artificial General Intelligence (AGI) Security [
Advanced Technologies and Societal Change, ],
► pp. 211 ff.
![DOI logo](//benjamins.com/logos/doi-logo.svg)
Kodura, Małgorzata
2024.
Rozwijanie kreatywności studentów na zajęciach z przekładu w epoce sztucznej inteligencji .
Półrocznik Językoznawczy Tertium 9:1
► pp. 274 ff.
![DOI logo](//benjamins.com/logos/doi-logo.svg)
This list is based on CrossRef data as of 31 december 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers.
Any errors therein should be reported to them.