HUMAN AND MACHINE TRANSLATION IN CONTEMPORARY TRANSLATION STUDIES
Keywords:
Human translation, Machine translation, Neural machine translation, Post-editing, Translation technology, Cross-cultural communication, Translation studies, Automated translation, Language processing, Translation qualityAbstract
The evolution of translation practices in the digital age has prompted increasing attention to the roles of human and machine translation. This study examines the characteristics, advantages, and limitations of both approaches, emphasizing their complementary functions within contemporary translation workflows. Machine translation, particularly neural machine translation (NMT), offers efficiency, scalability, and rapid processing of large volumes of text, but often struggles with context, idiomatic expressions, and cultural nuance. Human translation, in contrast, provides interpretive depth, cultural sensitivity, and stylistic accuracy, particularly in specialized or creative texts. The integration of machine translation with human post-editing emerges as a practical model, combining the speed of automated systems with the precision and adaptability of human expertise. The study concludes that collaboration between human translators and technological tools represents the most effective strategy for meeting the demands of globalized communication and multilingual information exchange.
References
1. Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
2. Catford, J. C. (1965). A linguistic theory of translation. Oxford University Press.
3. Garcia, I. (2015). Post-editing of machine translation: Processes and applications. John Benjamins Publishing Company.
4. Gambier, Y., & van Doorslaer, L. (2016). Handbook of translation studies (Vol. 2). John Benjamins Publishing Company.
5. House, J. (2015). Translation quality assessment: Past and present. Routledge.
6. Hutchins, W. J. (2005). Early years in machine translation: Memoirs and autobiographies of pioneers. Machine Translation, 19(3–4), 123–145.
7. Hutchins, W. J. (2013). Machine translation and human translators. Translation & Interpreting Studies, 8(1), 44–59.
8. Koehn, P. (2020). Neural machine translation. Cambridge University Press.
9. Munday, J. (2016). Introducing translation studies: Theories and applications (4th ed.). Routledge.
10. Ismatova Yu. Formy i metody raboty po formirovaniyu rechevykh navykov na osnove kreditno-modulnoy sistemy. Library. 2023;1(1):1–4.
