TYPES OF MACHINE TRANSLATION SYSTEMS: STATISTICAL, NEURAL, AND RULE-BASED APPROACHES.
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Abstract
Machine translation (MT) is a key technology in the field of Natural Language Processing (NLP) that enables computers to translate text from one language into another. Over time, different methods have been developed, including rule-based, statistical, and neural machine translation. This article discusses the essential principles behind each of these methods, comparing their core features, benefits, limitations, and areas of application.
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References
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4. Forcada, M. L., & Ñeco, R. P. (1997). Recursive hetero-associative memories for translation. Neurocomputing, 14(1), 1–9.
5. Jurafsky, D., & Martin, J. H. (2023). Speech and language processing (3rd ed.). Draft version. https://web.stanford.edu/~jurafsky/slp3/
6. Khujaev, D. G. (2025, May 19). Harnessing artificial intelligence in English and Uzbek language teaching in the era of digital transformation. Zenodo. https://doi.org/10.5281/zenodo.15466016
7. Koehn, P. (2010). Statistical machine translation. Cambridge University Press.
8. Lo, C. K. (2019). Yisi: A unified semantic MT evaluation metric for languages with different levels of available resources. In Proceedings of the Fourth Conference on Machine Translation (WMT 2019), 507–513.
9. Och, F. J., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.
10. Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems (NeurIPS), 27.
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