1887
Volume 69, Issue 4
  • ISSN 0521-9744
  • E-ISSN: 1569-9668
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Abstract

Abstract

This study examines the performance of the neural machine translation system DeepL in translating Shakespeare’s plays and . 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.

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2023-07-24
2025-02-09
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References

  1. 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 Translation5 (1): 151–169.
    [Google Scholar]
  2. Besacier, Laurent, and Lane Schwartz
    2015 “Automated Translation of A Literary Work: A Pilot Study.” InProceedings of NAACL-HLT Fourth Workshop on Computational Linguistics for Literature, 114–122. Denver, CO: Association for Computational Linguistics. 10.3115/v1/W15‑0713
    https://doi.org/10.3115/v1/W15-0713 [Google Scholar]
  3. Halliday, Michael, and Alexander Kirkwood
    2000An Introduction to Functional Grammar. Beijing: Foreign Language Teaching and Research Press.
    [Google Scholar]
  4. 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]
  5. Jones, Ruth, and Ann Irvine
    2013 “The (Un)Faithful Machine Translator.” InProceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, edited byPiroska Lendvai and Kalliopi Zervanou, 96–101. Sofia: Association for Computational Linguistics.
    [Google Scholar]
  6. 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]. InShashibiya 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 byShiqiu Liang, 5621. Taipei: Guoli bianyi guan.
    [Google Scholar]
  7. Matusov, Evgeny
    2019 “The Challenges of Using Neural Machine Translation for Literature.” InProceedings of the Qualities of Literary Machine Translation, edited byJames Hadley, Maja Popović, Haithem Afli, and Andy Way, 10–19. Dublin: European Association for Machine Translation.
    [Google Scholar]
  8. Palmer, Frank Robert
    1990Modality and the English Modals. London: Longman Group Limited.
    [Google Scholar]
  9. Toral, Antonio, and Andy Way
    2015 “Machine-assisted Translation of Literary Text: A Case Study .” Translation Spaces4 (2): 241–268. 10.1075/ts.4.2.04tor
    https://doi.org/10.1075/ts.4.2.04tor [Google Scholar]
  10. Vanmassenhove, Eva, Dimitar Shterionov, and Andy Way
    2019 “Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation.” InProceedings of Machine Translation Summit XVII Volume 1: Research Track, edited byMikel Forcada, Andy Way, Barry Haddow, and Rico Sennrich, 222–232. Dublin: European Association for Machine Translation.
    [Google Scholar]
  11. Van Brussel, Laura, Arda Tezcan, and Lieve Macken
    2018 “A Fine-grained Error Analysis of NMT, PBMT and RBMT Output for English-to Dutch.” InProceedings of the Eleventh International Conference on Language Resources and Evaluation, edited byNicoletta Calzolari , 3799–3844. Miyazaki: European Language Resources Association.
    [Google Scholar]
  12. Voigt, Rob, and Dan Jurafsky
    2012 “Towards a Literary Machine Translation: The Role of Referential Cohesion.” InThe 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), edited byEric Fosler-Lussier, Ellen Riloff, and Srinivas Bangalore, 18–25. Montréal: Association for Computational Linguistics.
    [Google Scholar]
  13. 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.” Informatics7 (3): 32. 10.3390/informatics7030032
    https://doi.org/10.3390/informatics7030032 [Google Scholar]
  14. Yang, Liu, and Min Zhang
    2015 “Statistical Machine Translation.” InThe Routledge Encyclopedia of Translation Technology, edited byChan Sin-wai, 201–213. New York: Routledge
    [Google Scholar]
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