Human-made vs. neural machine translation: A comparative analysis of human-made and machine-translated literary texts
Abstract
There seems to be a consensus that register-specific, informative texts are more suitable for automated machine translation, while form-focused texts are less so. Since there are limitations in machine translation in terms of communicative and translation competence, texts in which linguistic form, pragmatic meanings, connotations, and culture-specific elements play an important role, in addition to content, are more difficult for machine translation programs to cope with. In this paper, I will attempt to demonstrate the relevant differences that arise in the process of machine and human translation by comparing a neural machine translation (by DeepL) of a literary text (“Fatelessness” written by Imre Kertész) with the human translated text (by Tim Wilkinson). The ultimate goal of my research is to gain more insight into the quality of Hungarian–English machine translation, how corpus linguistic analysis of the source and target languages can be of further use, and what are the limitations (if any) of the use of machine translation in the translation and post-editing of literary texts.
References
Dimitrouila, T. (2022): Corpora and Literary Translation. In: Moratto, R. – Li, D. (eds) (2022): Advances in Corpus Applications in Literary and Translation Studies. 103–119. Routledge.
Donaj, G. – Antloga, S. (2023): ParaDiom – A Parallel Corpus of Idiomatic Texts. The Version of Record of this contribution is published in Proceedings of Text, Speech, and Dialogue – 26th International Conference, TSD 2023, Pilsen, Czech Republic, September 4–6, 2023.
https://doi.org/10.1007/978-3-031-40498-67
Farkas, A. – Németh, R. (2022): How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points. Social Sciences & Humanities Open. 5/1.
https://doi.org/10.1016/j.ssaho.2021.100239
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 Conference on Language Resources and Evaluation (LREC 2020), Marseille, 11–16 May 2020, 3790–3798.
https://aclanthology.org/2020.lrec-1.468.pdf.
Hadley, J. (2020): Literary machine translation: Are the computers coming for our jobs? Counterpoint. 4. 14–18.
Jurafsky, D. – Martin, J.H. (2023): Speech and Language Processing. Draft of February 3, 2024, p. 2.
https://web.stanford.edu/~jurafsky/slp3/3.pdf
Kenny, D. (2022): Human and machine translation. In: Kenny, D. (ed.) (2022): Machine translation for everyone: Empowering users in the age of artificial intelligence. (Translation and Multilingual Natural Language Processing 18). 23–4. Berlin: Language Science Press.
Loock, R. (2020): No more rage against the machine: How the corpus-based identification of machine-translationese can lead to student empowerment? The Journal of Specialised Translation. 34. 150–170.
Prates, M.O.R. – Avelar, P.H. – Lamb, L.C. (2020): Assessing gender bias in machine translation: a case study with Google Translate. Neural Computing and Applications. 32. 6363–6381.
https://doi.org/10.1007/s00521-019-04144-6
Rescigno, A.A. et al. (2020): A Case Study of Natural Gender Phenomena in Translation: A Comparison of Google Translate, Bing Microsoft Translator and DeepL for English to Italian, French and Spanish. In: Workshop on the Impact of Machine Translation (iMpacT 2020). 62–90.
Sánchez-Gijón, P. – Kenny, D. (2022): Selecting and preparing texts for machine translation: Pre-editing and writing for a global audience. In Kenny, D. (ed). (2022): Machine translation for everyone: Empowering users in the age of artificial intelligence. (Translation and Multilingual Natural Language Processing 18). 81–105. Berlin: Language Science Press.
Teixeira, C. (2020): Revising computer-mediated translations. In: Mossop, B. – Hong, J. – Teixeira, C. (eds) (2020): Revising and Editing for Translators. Routledge. https://doi.org/10.4324/9781315158990-16
Tempus Public Foundation, Hungarian Nobel Prize Winners – Imre Kertész, 2020.
https://studyinhungary.hu/blog/hungarian-nobel-prize-winners-imre-kertesz
Yamada, M. (2019): The impact of Google Neural Machine Translation on post-editing by student translators. The Journal of Specialised Translation. 31. 87–106.
