Artificial intelligence in translating phraseological units of a literary text

The article reveals the essence and specifics of applying artificial intelligence technologies in the translation into English of Russian phraseological units used in the context of poetic works (based on the poems of S. Yesenin). The experiment tests the translation engines of statistical, neural and hybrid types represented by the DeepL API, Google Neural Machine Translation and PROMT platforms, respectively. The article presents a comparative analysis of the translations made with the help of each of the engines under consideration and the literary translation performed by people. The engines that exist at the moment are not perfect, which determines their popularity at the pre-translation stage, while professional literary translation still remains the prerogative of relevant specialists, implying the need for careful editing of the machine translated product. Special attention in the article is paid to the pragmatic component, which, when working with machine translation, is one of the key characteristics of phraseological constructions, in particular, the possibilities of preserving it in a context adequate to the original. The research was carried out due to the significance of the topic, the active introduction of artificial intelligence technologies into the life of a modern person and the rapid growth of popularity of digital translation, which has confidently defined its position in global translation practice and relevant theoretical developments.

1. Vinogradov, V. V. (1972). Russkii yazyk [The Russian Language]. 613 p. Moscow, Vysshaya shkola. (In Russian)

2. Vinogradov, V. V. (1977). Ob osnovnykh tipakh frazeologicheskikh edinits v russkom yazyke [About the Main Types of Phraseological Units in the Russian Language]. Izbrannye trudy: leksikologiya i leksiko-grafiya. Pp. 140–161. Moscow. (In Russian)

3. Derbukova, T. N. (1960). K opredeleniyu ob"ema ponyatiya frazeologicheskogo sochetaniya [On Defining the Capacity of Phraseological Combination Meaning]. The Ural State University. Uchyonye zapiski. Vyp. 30. Yazykoznanie. 182 p. Sverdlovsk. (In Russian)

4. Kunin, A. V. (1970). Angliiskaya frazeologiya [English Phraseology]. 344 p. Moscow, Vysshaya shkola. (In Russian)

5. Shansky, N. M. (1985). Frazeologiya sovremennogo russkogo yazyka [Phraseology of the Modern Russian Language]. 169 p. Moscow, Vysshaya shkola. (In Russian)

6. McLuhan, M. (2003). Ponimanie Media: Vneshnie rasshireniya cheloveka [Understanding Media: The Extensions of Man]. 464 p. Moscow, Kanon-press-C; Kuchkovo pole. (In Russian)

7. Cronin, M. (2010). The Translation Crowd. Revista Tradumatica, 2010. URL: https://www.researchgate.net/publication/266161999_The_Trans-lation_Crowd (accessed: 28.07.2024). (In English)

8. Boyarkina, A. V. (2022). Tsifrovye tekhnologii v khudozhestvennom perevode (na primere nemetskoyazychnykh tekstov) [Digital Technologies in Literary Translation: A Case Study of Texts in German]. Izvestiya Rossiyskogo gosudarstvennogo pedagogicheskogo universiteta im. A. I. Gertsena. No. 205, pp. 14–20. (In Russian)

9. Garbovskiy, N. K. (2019). “Tsifrovoi perevod”. Sovremennye realii i prognozy [“Digital Translation”. Present-Day Realities and Forecasts]. Russkii yazyk i kul'tura v zerkale perevoda [Russian Language and Culture in the Mirror of Translation]. No. 1, pp. 65–72. (In Russian)

10. Gudiy, K. A. (2021). Konkurentsiya perevodchika frantsuzskogo yazyka s robotom: prognozy i perspektivy [The Competition of French Language Translator with the Robot: Prospects and Forecasts]. Vestnik Moskovskogo universiteta. Seriya 22. Teoriya perevoda. No. 2, pp. 4–11. (In Russian)

11. Besacier, L., Schwartz, L. (2015). Automated Translation of a Literary Work: A Pilot Study. Proceedings of the Fourth Workshop on Computational Linguistics for Literature, pp. 114–122. (In English)

12. Fonteyne, M., Tezcan, A., Macken, L. (2020). Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-translated Detective Novel on Document Level. Proceedings of the 12th Language Resources and Evaluation Conference, pp. 3790–3798. Paris, European Language Resources Association Publ. (In English)

13. Kenny, D., Winters, M. (2020). Machine Translation, Ethics and the Literary Translator's Voice. Translation Spaces, 9/1, pp. 123–149. Amsterdam, John Benjamins Publ. (In English)

14. Matusov, E. (2019). The Challenges of Using Neural Machine Translation for Literature. The Qualities of Literary Machine Translation. Dublin. URL: https://www.aclweb.org/anthology/W19-7302.pdf (accessed: 13.07.2024). (In English)

15. Taivalkoski-Shilov, K. (2019). Ethical Issues Regarding Machine(-assisted) Translation of Literary Texts. Perspectives. Studies in Translation Theory and Practice. Vol. 27. No. 5, pp. 689–703. (In English)

16. Omniscien Technologies. URL: https://omniscien.com/ (accessed: 13.07.2024). (In English)

17. Banitz, B. (2020). Machine Translation: A Critical Look at the Performance of Rule-Based and Statistical Machine Translation. Cad. Tradução. No. 40, pp. 54–71. (In English)

18. Yesenin, S. A. (1983). Sobranie sochinenii v 3 tomakh [Collected Works in 3 Vols.]. Red. Y. L. Prokushev. Tom 1. 384 p. Moscow, Pravda. (In Russian)

19. DeepL. URL: https://www.deepl.com/ru/translator (accessed: 15.09.2024). (In Russian)

20. Google Translate. URL: https://translate.google.com/ (accessed: 15.09.2024). (In Russian)

21. PROMT. One. URL: https://www.translate.ru/ (accessed: 15.09.2024). (In Russian)

22. RuVerses. URL: https://ruverses.com/sergey-esenin/ (accessed: 15.09.2024). (In Russian)

23. Frazeologicheskii slovar' russkogo yazyka (2001) [Phraseological Dictionary of the Russian Language]. Red. A. I. Molotkov, L. A. Voinova. 510 p. Moscow, AST, Astrel. (In Russian)

24. Multitran. URL: https://www.multitran.com/ (accessed: 15.09.2024). (In Russian)