UTILIZING MACHINE TRANSLATION TECHNOLOGY FOR REPRODUCING SHORT PROSE LITERARY TEXTS

Evelina Levonivna Sarhsian, evelina.sarhsian@karazin.ua

lecturer

N. Karazin Kharkiv National University

Olha Andriivna Zinchenko, olha.zinchenko1@gmail.com

lecturer

N. Karazin Kharkiv National University

DOI: https://doi.org/10.17721/StudLing2024.24.101-110

PDF (UKRAINIAN)

ABSTRACT

 Machine translation systems permeate all spheres of human activity, including the field of literary translation. Scholars note that machine translation holds significant potential for development and is less labor-intensive compared to the work of human translators. Consequently, there is a need to analyze common errors in machine translation to help avoid them in the future as neural machine translation systems continue to evolve.

This article examines and analyzes the quality of a Ukrainian translation of the Spanish short story “Amigos” by Argentine writer Julio Cortázar, performed by the machine translation system DeepL. The translated text was compared with the official Ukrainian translation done by a human translator. The translated text was analyzed for errors, and the types of errors made by the DeepL system were identified. Additionally, the number of errors was counted. The study employed methods of analysis, synthesis, and comparison of the original and translated texts. Considering the number and significance of the errors in terms of their impact on the essence of the text within narrow and broad contexts, we concluded that the quality of the story’s translation is relatively high. It was determined that using machine translation for translating literary works, particularly short prose, is potentially feasible and effective, provided that the translated text is subsequently edited by a human translator. To improve the quality of machine translation of literary texts, recommendations were developed to enhance the performance of the DeepL machine translation system.

Keywords: machine translation, translation errors, literary text, narrow context, broad context, human translator, DeepL machine translation system.

References:

  1. Cortázar, J. «Таyemnа zbroya. Zbirka [Secret weapon. Collection]». Dnipro (1983): 21-23.
  2. Abdulfattah, O.; Yasser, G. «The Machine Translation of Literature: Implications for Translation Pedagogy». In International Journal of Emerging Technologies in Learning 15(11) (2020): 228-235. URL: https://online-journals.org/index.php/i-jet/article/view/13275/715.
  3. Benjamins, J. «Creativity in translation». John Benjamins Publishing Company (2022): 184-212. URL: https://www.jbe-platform.com/.
  4. Kenny, D.; Winters, М. «Machine translation, ethics and the literary translator’s voice». In Translation Spaces, vol. 9(1) (2020): 123-149. URL: https://pure.hw.ac.uk/ws/portalfiles/portal/41374556/Kenny_Winters_Translation_Spaces_Article_a ccepted_24_April.pdf.
  5. Kenny, D. «Human and machine translation. Machine translation for everyone: Empowering users in the age of artificial intelligence». Berlin: Language Science Press (2022): 23-49.
  6. Macan J.; Mitkov R., Olaf-Michael, S. “Translating and The Computer”, In J. Esteves-Ferreira 36 (2014). URL: https://aclanthology.org/2014.tc-1.23.pdf.
  7. Moorkens, J. «What to expect from Neural Machine Translation: a practical in class translation evaluation exercise». The Interpreter and Translator Trainer, v. 12, n. 4, (2018): 375–387. URL: https://doi.org/10.1080/1750399X.2018.1501639.
  8. Toral, А.; Way, А. 2014. «Is machine translation ready for literature?» In J. Esteves-Ferreira (2014). URL: https://aclanthology.org/2014.tc-1.23.pdf.
  9. oral, W.; Way A., Toral, A.; Wieling, M. «Post-editing Effort of a Novel with Statistical and Neural Machine Translation». Frontiers in Digital Humanities v. 5 (2018): 1–11. URL: https://doi.org/10.3389/fdigh.2018.00009.
  10. https://cdn.aaai.org/ocs/7864/7864-36722-1-PB.pdf
  11. .ERIC – EJ1089886 – A Comparative Study of “Google Translate” Translations: An Error Analysis of English-to-Persian and Persian-to-English Translations, English Language Teaching, 2016 (ed.gov)
  12. Automatic Translations Versus Human Translations in Nowadays World – ScienceDirect
  13. https://www.taylorfrancis.com/chapters/edit/10.4324/9781315648934-18/issues-human-automatic-translation-quality-assessment-stephen-doherty
  14. Microsoft Word – V2S1 English papers (researchgate.net) [2105.04616] Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity (arxiv.org)
  15. https://narrativabreve.com/2014/09/cuento-de-julio-cortazar-los-amigos.html
  16. https://bul.in.ua/k/kortasar-hulio/druzi-2/
  17. https://www.deepl.com/translator
  18. https://gotranscript.com/text-compare#diff