ARTIFICIAL INTELLIGENCE IN TRANSLATION: TECHNOLOGIES, IMPLICATIONS, AND FUTURE DIRECTIONS

Raxmatullayeva Sayyora Jonzoq kizi

Authors

Raxmatullayeva Sayyora Jonzoq kizi O‘zDJTU o‘qituvchisi

Keywords:

artificial intelligence, machine translation, neural machine translation, transformer architecture, human translators, post-editing, translation ethics, multilingual communication, cultural context.

Abstract

The integration of artificial intelligence into translation has significantly transformed linguistic mediation, multilingual communication, and professional translation practice. This article examines the technological foundations, practical applications, limitations, ethical concerns, and future directions of AI-powered translation systems. The study focuses particularly on neural machine translation, transformer-based architectures, attention mechanisms, multilingual modeling, and human-AI collaboration. The findings show that AI translation improves speed, scalability, accessibility, and cost efficiency in global communication. However, it still faces serious challenges in translating idioms, humor, figurative language, culturally specific meanings, legal and medical texts, and low-resource languages. Ethical issues such as bias, privacy, data security, and overreliance on automated systems also remain significant.

References

Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate.

DeepL Translator Research. (2023). Neural network approaches in modern machine translation.

European Commission. (2020). Language Technology and AI Report.

Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks.

UNESCO. (2021). AI and Cultural Diversity.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.