CAN ARTIFICIAL INTELLIGENCE REPLACE TRANSLATORS?

Authors

  • Izzatova Dildora Xasan qizi 2nd year student of Uzbekistan State World Languages University Translation faculty dildoraizzatova8@gmail.com Author

Keywords:

artificial intelligence, translation, human translators, language, cultural context, translation limitations.

Abstract

With the rapid development of artificial intelligence (AI), its role in language translation has become increasingly prominent. This article examines whether AI can fully replace human translators. With the rapid rise of   Al translation has become faster and more accessible. For example, Al tools can quickly render simple technical manuals or news articles. However, they often struggle with subtle cultural references, idiomatic Uzbek expressions, or emotional tones in literary texts. This paper examines both the advantages and limitations of Al in translation. While Al can handle large volumes efficiently, human translators remain essential for ensuring accurate, culturally appropriate, and nuanced communication. Al is therefore a valuable assistant but cannot entirely replace human translators. The paper discusses the strengths and limitations of AI in translation and highlights the ongoing importance of human expertise in ensuring accurate and culturally appropriate communication. Ultimately, AI serves as a powerful support tool but cannot entirely replace the human translator.

References

1. Aziz, W., et al. (2020). Advances in Neural Machine Translation. Computational Linguistics, 46(1), 87–136.

2. Bowker, L. (2018). Computer-Aided Translation. Routledge.

3. Castilho, S., & Gasperin, C. (2019). Machine Translation Post-Editing: Human Performance and Productivity. Translation & Interpreting, 11(2), 30–51.

4. García, I. (2020). Translation and Artificial Intelligence: A Survey. Journal of Translation Studies, 12(3), 45–62.

5. Johnson, M., et al. (2017). Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Transactions of the Association for Computational Linguistics, 5, 339–351.

6. Koehn, P. (2020). Neural Machine Translation. Cambridge University Press.

7. O‘Hagan, M. (2019). Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Journal of Information Science.

8. Somers, H. (2012). Computers and Translation: A Translator’s Guide. John Benjamins Publishing Company.

9. Specia, L., & Turchi, M. (2014). Machine Translation Evaluation. Synthesis Lectures on Human Language Technologies.

10. Toral, A., & Way, A. (2018). What Level of Quality Can Neural Machine Translation Attain on Literary Text?. Transactions of the Association for Computational Linguistics, 6, 107–120.

11. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Proceedings of NeurIPS.

12. Zaretskaya, T. (2021). Human vs Machine Translation: Cognitive and Cultural Perspectives. International Journal of Language Studies.

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Published

2026-07-15