PROBLEMS OF MACHINE TRANSLATION AND ITS APPLICATION IN TRANSLATOR TRAINING

Authors

Keywords:

human translation, machine translation, trans-human translation, translator training and teaching.

Abstract

The article reviews the current research related to the status of machine translation (MT) and its application in translator training. The author states the increasing impact of technologies upon the translation and localisation industries, which gives grounds to expect considerable changes in the human translation process that have to be taken into account in translator training as well. One of the ways to deal with this issue seems to be the comprehensive approach to the MT integration into translator training. In spite of the contradictory data related to the research into the comparative efficiency of human and machine translation, on the one hand, and various kinds of MT systems, on the other, the paper comes to the conclusion that there is no alternative to the MT application in professional translation, and thus, to its involvement into the translator training as well. The author supports the idea of trans-human translation, where the human remains in charge, skillfully exploiting the technological advantages to the benefit of the translation speed and quality. This approach takes Translation Studies beyond the limits of purely linguistic or anthropocentric models. The author analyses the controversial opinion concerning shifting the emphasis in the translator’s work towards the linguistic post-post-editing. This assumption is used to predict the decline in the translator’s subject knowledge importance and even in the target language command requirements related to the ever growing practice of the target text post-editing by experts in the specific domain.. The author reviews the role of pre-editing, post-editing and proofreading in the eyes of the translation and localisation industry representatives, as well as the ways to include the appropriate skills development in translator-training. The article analyses the conflicting results concerning the impact of post-editing upon the efficiency of the translator’s competence development. It is concluded that translators will have to possess a variety of innovative skills, which might open up promising career opportunities for them, subject to the consolidation of interdomain and interdisciplinary connections in translator training and teaching.

References

Alonso, E. & Calvo, E. (2015). Developing a Blueprint for a Technology-mediated Approach to Translation Studies. Meta, 60(1), 135–157. https://doi.org/10.7202/1032403a

Burchardt, A., Lommel, A., Bywood, L., Harris, K. & Popović, M. (2016). Machine translation quality in an audiovisual context. Target, 28(2), 206-221. https://doi.org/10.1075/target.28.2.03bur

Cid, C. G., Colominas, C., Oliver, A. (2020). Language industry views on the profile of the posteditor. Translation Spaces, 9(2), 283-313. https://doi.org/10.1075/ts.19010.cid

Cifuentes-Ferez P., Rojo, A. (2015). Thinking for Translating. Think-aloud Protocol on the translation of manner-of-motion verbs. Target, 27(2), 273-300. https://doi.org/10.1075/target.27.2.05cif

Daems, J., Vandepitte, S., Hartsuiker, R. J. & Macken, L. (2017). Translation Methods and Experience: A Comparative Analysis of Human Translation and Post-editing with Students and Professional Translators. Meta, 62(2), 245–270. https://doi.org/10.7202/1041023ar

EMT Expert Group (2009). Competences for professional translators, experts in multilingual and multimedia communication. Brussels. https://ec.europa.eu/info/sites/info/files/emt_competences_translators_en.pdf

Esqueda, M. D. (2021). Machine translation: teaching and learning issues. Trabalhos em Linguística Aplicada, 60(1). 282-299.

Federici, F. M., Al Sharou, Kh. (2018). Moses, time, and crisis translation. Translation and Interpreting Studies, 13(3), 486-508. https://doi.org/10.1075/tis.00026.fed

Gaspari, F., Almaghout, H. & Doherty, S. (2015). A survey of machine translation competences: Insights for translation technology educators and practitioners. Perspectives, 23(3), 333-358. https://doi.org/10.1080/0907676X.2014.979842

Jisun, Sh., Kim E. (2017). The Emergence of an Artificial Intelligence Translation System. The Journal of Translation Studies, 18(5), 91-110

Kenny, D., Doherty, S. (2014). Statistical machine translation in the translation curriculum: overcoming obstacles and empowering translators. The Interpreter and Translator Trainer, 8(2), 276-294. https://doi.org/10.1080/1750399X.2014.936112

Killman, J. (2018). Translating the same text twice. The Journal of Internationalization and Localization, 5(2), 114-141. https://doi.org/10.1075/jial.18003.kil

Lee, S.-M. (2020). The impact of using machine translation on EFL students’ writing. Computer Assisted Language Learning. 33(3). 157-175. https://doi.org/10.1080/09588221.2018.1553186.

Loock, R. (2018). Traduction automatique et usage linguistique : une analyse de traductions anglaisfrançais réunies en corpus. Meta, 63(3), 786–806. https://doi.org/10.7202/1060173ar

Man, D., Mo, A., Chau, M. H., O’Toole, J. M., Lee, Ch. (2020). Translation technology adoption: evidence from a postgraduate programme for student translators in China. Perspectives, 28(2), 253-270. https://doi.org/10.1080/0907676X.2019.1677730

Marshman, E. (2014). Taking Control: Language Professionals and Their Perception of Control when Using Language Technologies. Meta, 59(2), 380–405. https://doi.org/10.7202/1027481a

Mellinger, Ch. D. (2017) Translators and machine translation: knowledge and skills gaps in translator pedagogy. The Interpreter and Translator Trainer. 11(4). 280-293. https://doi.org/10.1080/1750399X.2017.1359760

Mellinger D. Ch. (2018). Re-thinking translation quality. Revision in the digital age. Target, 30(2), 310-331. https://doi.org/10.1075/target.16104.mel

Moorkens, J. (2018). What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise. The Interpreter and Translator Trainer, 12(4), 375-387. https://doi.org/10.1080/1750399X.2018.1501639

O’Brien, Sh., Rossetti, A. (2020). Neural machine translation and the evolution of the localisation sector. The Journal of Internationalization and Localization, 7(1-2), 95-121. https://doi.org/10.1075/jial.20005.obr

PACTE Group (2003). Building a Translation Competence Model. Triangulating Translation: Perspectives in Process Oriented Research /Alves F. (ed.). Amsterdam: Benjamins. P. 43-66. https://doi.org/10.1075/btl.45.06pac

Pym, A. (2013). Translation Skill-Sets in a Machine-Translation Age. Meta, 58(3), 487–503. https://doi.org/10.7202/1025047ar

Rodríguez-Castro, M. (2018). An integrated curricular design for computer-assisted translation tools: developing technical expertise. The Interpreter and Translator Trainer. 12(4). 355-374. https://doi.org/10.1080/1750399X.2018.1502007

Rodríguez de Céspedes, B. (2019). Translator Education at a Crossroads: the Impact of Automation. Lebende Sprachen, 64(1), 103-121. https://doi.org/10.1515/les-2019-0005

Şahin, M. & Duman, D. (2013). Multilingual Chat through Machine Translation: A Case of English- Russian. Meta, 58(2), 397–410. https://doi.org/10.7202/1024180a

Schmidhofer, A. & Mair, N. (2019). La traducción automática en la formación de traductores. CLINA: Revista Interdisciplinaria de Traducción, Interpretación y Comunicación Intercultural, 4(2), 163-180. https://doi.org/10.14201/clina201842163180

Shih, C. Y. (2017). Web search for translation: an exploratory study on six Chinese trainee translators’ behaviour. Asia Pacific Translation and Intercultural Studies, 4(1), 50-66.

https://doi.org/10.1080/23306343.2017.1284641

Vieira L. N. & Alonso E. (2020) Translating perceptions and managing expectations: an analysis of management and production perspectives on machine translation. Perspectives, 28(2), 163-184. https://doi.org/10.1080/0907676X.2019.1646776

Yang, Y., Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116-126. https://doi.org/10.1016/j.compedu.2019.01.015

Yang, Y. & Wang X. (2020) Predicting student translators’ performance in machine translation postediting: interplay of self-regulation, critical thinking, and motivation. Interactive Learning Environments, 0(0), 1-15. https://doi.org/10.1080/10494820.2020.1786407

Yang, Y., Wang X. & Yuan Q. (2021). Measuring the usability of machine translation in the classroom context. Translation and Interpreting Studies, 16(1), 101-123. https://doi.org/10.1075/tis.18047.yan

이상빈. (2018). Process research into post-editing: How do undergraduate students post-edit the output of Google Translate? The Journal of Translation Studies, 19(3), 259-286. https://doi.org/10.15749/jts.2018.19.3.010

Published

2023-03-31

How to Cite

CHERNOVATY, L. (2023). PROBLEMS OF MACHINE TRANSLATION AND ITS APPLICATION IN TRANSLATOR TRAINING. Наукові записки. Серія: Філологічні науки, (202), 84–93. Retrieved from https://journals.cusu.in.ua/index.php/philology/article/view/13