DEVELOPMENT OMACHINE TRANSLATION IN MODERN WORLD
DOI:
https://doi.org/10.54613/ku.v19iA.1806Keywords:
machine translation, automated translation, translation accuracy, communicative equivalence, contextual adequacy, linguistic technologies.Abstract
This article examines the development of machine translation, its technological evolution, and its role in modern multilingual communication. Particular attention is given to translation accuracy, contextual adequacy, communicative equivalence, and the challenges of representing domain-specific and culturally conditioned linguistic features. The study additionally focuses on the comparative evaluation of contemporary machine translation services through quantitative and qualitative approaches. Particular emphasis is placed on identifying discrepancies between automatic evaluation scores and expert judgments, especially in cases involving paraphrasing, specialized terminology, syntactic restructuring, and context-dependent meanings. The research also considers the applicability of machine translation to scientific and medical discourse and highlights the importance of post-editing in achieving terminological precision and communicative adequacy. The findings contribute to a more comprehensive understanding of the practical capabilities and limitations of current automated translation technologies. The article further addresses the challenges associated with translating culturally marked expressions, low-frequency lexical units, and terminology that requires specialized contextual interpretation. It emphasizes the need to consider not only linguistic similarity but also semantic relations, discourse structure, and the intended communicative function of the source text. The study highlights the potential of combining neural machine translation with human expertise as a practical approach to improving the consistency, precision, and usability of automated translation in academic and professional communication.
Foydalanilgan adabiyotlar:
1. Карцева Е. Ю., Маргарян Т. Д., Гурова Г. Г. Развитие машинного перевода и его место в профессиональной межкультурной коммуникации //Вестник Российского университета дружбы народов. Серия: Теория языка. Семиотика. Семантика. – 2016. – №. 3. – С. 155-164.
2. Borisov A., Galinskaya I. Y. Yandex School of Data Analysis Russian-English Machine Translation System for WMT14 // Proceedings of the Ninth Workshop on Statistical Machine Translation. – 2014. – P. 66–70.
3. Cambedda G., Di Nunzio G. M., Nosilia V. A Study on Automatic Machine Translation Tools: A Comparative Error Analysis Between DeepL and Yandex for Russian-Italian Medical Translation // Umanistica Digitale. – 2021. – No. 10. – P. 139–163.
4. Kulikov V., Kulikova V., Yerkebulan G. Google/Yandex Translation Detection in the Patterns Identifying System of Multilingual Texts // International Journal of Computers. – 2021. – Vol. 20, No. 1. – P. 72–77.
5. Kamaluddin M. I. et al. Accuracy Analysis of DeepL: Breakthroughs in Machine Translation Technology // Journal of English Education Forum (JEEF). – 2024. – Vol. 4, No. 2. – P. 122–126.
6. Linlin L. Artificial Intelligence Translator DeepL Translation Quality Control // Procedia Computer Science. – 2024. – Vol. 247. – P. 710–717.
7. Sriram A. et al. Cold Fusion: Training Seq2Seq Models Together with Language Models. – 2017. – arXiv:1708.06426.
8. Egonmwan E., Chali Y. Transformer and Seq2Seq Model for Paraphrase Generation // Proceedings of the 3rd Workshop on Neural Generation and Translation. – 2019. – P. 249–255.
9. Li Z. et al. Seq2Seq Dependency Parsing // Proceedings of the 27th International Conference on Computational Linguistics. – 2018. – P. 3203–3214.
10. Sutskever I. Sequence to Sequence Learning with Neural Networks. – 2014. – arXiv:1409.3215.
11. Ni J. et al. Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. – 2021. – arXiv:2108.08877.
12. Chen G. et al. Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation // Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. – 2022. – Vol. 1. – P. 142–157.
13. Rothman D. Transformers for Natural Language Processing: Build, Train, and Fine-Tune Deep Neural Network Architectures for NLP with Python, Hugging Face, and OpenAI’s GPT-3, ChatGPT, and GPT-4. – Birmingham : Packt Publishing Ltd, 2022.
14. Maltas S. I. et al. Efficient Finetuning Strategies for Multilingual Neural Machine Translation. – Barcelona : Universitat Politècnica de Catalunya, 2024.
15. Bengesi S. et al. Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers // IEEE Access. – 2024. – Vol. PP, No. 99. – P. 1–1.
16. Achiam J. et al. GPT-4 Technical Report. – 2023. – arXiv:2303.08774.
17. Agarwal A., Lavie A. METEOR, M-BLEU and M-TER: Evaluation Metrics for High-Correlation with Human Rankings of Machine Translation Output // Proceedings of the Third Workshop on Statistical Machine Translation. – 2008. – P. 115–118.
18. Babych B., Hartley T. Extending the BLEU MT Evaluation Method with Frequency Weightings // Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04). – 2004. – P. 621–628.
19. Chen B., Cherry C. A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU // Proceedings of the Ninth Workshop on Statistical Machine Translation. – 2014. – P. 362–367.
20. Papineni K. et al. BLEU: A Method for Automatic Evaluation of Machine Translation // Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. – 2002. – P. 311–318.
21. Pérez D., Alfonseca E. Application of the BLEU Algorithm for Recognising Textual Entailments // Proceedings of the First Challenge Workshop Recognising Textual Entailment. – 2005. – P. 9–12.
22. Сарсенбаева З. Analysis of images and symbols in english non-realistic works //Ренессанс в парадигме новаций образования и технологий в XXI веке. – 2023. – Т. 1. – №. 1. – С. 229-232.
23. Сарсенбаева З. Модернизм в узбекской литературе и интерпретация образов //Зарубежная лингвистика и лингводидактика. – 2024. – Т. 2. – №. 1. – С. 193-199.
24. Zoya S. LITERARY ANALYSIS OF DAVID MITCHELL’S WORKS IN ENGLISH LITERATURE //Talqin va tadqiqotlar ilmiy-uslubiy jurnali. – 2024. – Т. 2. – №. 41. – С. 11-19.
25. Zoya S. LITERARY ANALYSIS OF ULUGBEK HAMDAMOV’S WORKS IN UZBEK LITERATURE //Talqin va tadqiqotlar ilmiy-uslubiy jurnali. – 2024. – Т. 2. – №. 41. – С. 20-25.
26. Sаrsеnbаеvа Z. MODERN APPROACHES TO TEACHING LITERARY TERMS IN COMPARATIVE LITERATURE //Mental Enlightenment Scientific-Methodological Journal. – 2026. – Т. 7. – №. 02. – С. 198-209.
27. Belgibayeva G., Sarsenbaeva Z., Zhumagulova K. MAG ‘JAN JUMABAYEV IJODIDA OZODLIK G ‘OYASI //Ijtimoiy-gumanitar sohada ilmiy-innovatsion tadqiqotlar. – 2026. – Т. 3. – №. 1. – С. 159-165.
28. Sarsenbayeva Z., Zillolova G. COMPARATIVE ANALYSIS OF TWO TRANSLATED VERSIONS OF A LITERARY TEXT //Ijtimoiy-gumanitar sohada ilmiy-innovatsion tadqiqotlar. – 2025. – Т. 2. – №. 4. – С. 183-188.
29. Sarsenbayeva Z., Zillolova G. СРАВНИТЕЛЬНЫЙ АНАЛИЗ ДВУХ ПЕРЕВОДНЫХ ВЕРСИЙ ХУДОЖЕСТВЕННОГО ТЕКСТА //Scientific and innovative research in the social and humanitarian sphere. – 2025. – Т. 2. – №. 4. – С. 183-188.
30. Stap D., Araabi A. ChatGPT Is Not a Good Indigenous Translator // Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP). – 2023. – P. 163–166.
Downloads
Published
Iqtiboslik olish
Issue
Section
License
Copyright (c) 2026 QO‘QON UNIVERSITETI XABARNOMASI

This work is licensed under a Creative Commons Attribution 4.0 International License.