DOI
https://doi.org/10.47689/2181-3701-vol2-iss4/S-pp203-208Ключевые слова
машинный перевод / нейронный машинный перевод / Узбекистан / Центральная Азия / корпусное построение / языки с низкими ресурсамиАннотация
Машинный перевод (МП) достиг значительных успехов в глобальном масштабе, однако для языков с низкими ресурсами, таких как узбекский, сохраняются определённые проблемы. Несмотря на достижения в области нейронного машинного перевода (НМП) и глубокого обучения, специфические языковые трудности, такие как нехватка данных, синтаксическая сложность и морфологическое богатство, продолжают препятствовать прогрессу. В данной статье рассматриваются развитие и применение МП в Узбекистане, а также вклад узбекских и международных исследователей в эту область. Анализируется текущее состояние МП, выделяются проблемы, характерные для узбекского языка, и предлагаются направления для будущих исследований с акцентом на создание корпусов, нейронные модели и оценочные метрики.
Библиографические ссылки
Abduraxmonova, N. Z. "Linguistic support of the program for translating English texts into Uzbek (on the example of simple sentences): Doctor of Philosophy (PhD) il dis. aftoref." (2018).
Abdurakhmonova N. The bases of automatic morphological analysis for machine translation. Izvestiya Kyrgyzskogo gosudarstvennogo tekhnicheskogo universiteta. 2016;2 (38):12-7.
Abdurakhmonova N, Tuliyev U. Morphological analysis by finite state transducer for Uzbek-English machine translation/Foreign Philology: Language. Literature, Education. 2018(3):68.
Abdurakhmonova N, Urdishev K. Corpus based teaching Uzbek as a foreign language. Journal of Foreign Language Teaching and Applied Linguistics (J-FLTAL). 2019;6(1-2019):131-7.
Abdurakhmonov N. Modeling Analytic Forms of Verb in Uzbek as Stage of Morphological Analysis in Machine Translation. Journal of Social Sciences and Humanities Research. 2017;5(03):89-100.
Kubedinova L. Khusainov A., Suleymanov D., Gilmullin R., Abdurakhmonova N. First Results of the TurkLang-7 Project: Creating Russian-Turkic Parallel Corpora and MT Systems. Proceedings of the Computational Models in Language and Speech Workshop (CMLS 2020) co-located with 16th International Conference on Computational and Cognitive Linguistics (TEL 2020) .2020/11: 90-101
Abdurakhmonova N. Dependency parsing based on Uzbek Corpus. InProceedings of the International Conference on Language Technologies for All (LT4All) 2019Abdullaev, M. (2018). Challenges of morphological analysis in Uzbek machine translation. Tashkent State University Journal of Computational Linguistics, 12(4), 45-56.
Akhmedov, I. (2020). Improving machine translation quality with domain-specific corpora for Uzbek. Proceedings of the International Conference on Computational Linguistics, 34-40.
Azizov, A. (2021). Expanding the Uzbek corpus for machine translation: Challenges and opportunities. Journal of Uzbek Linguistics and Computational Science, 13(1), 29-42.
Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural machine translation by jointly learning to align and translate. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015). https://openreview.net/forum?id=SyxKx8bxl
Banerjee, S., & Lavie, A. (2005). METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), 311-318.
Jensen, K., Cohn, T., & Blunsom, P. (2003). Statistical machine translation for Central Asian languages: Challenges and progress. Proceedings of the 10th International Workshop on Spoken Language Translation (IWSLT 2003), 102-108.
Iskanderov, F. (2019). Automatic quality assurance systems in Uzbek machine translation. Journal of Artificial Intelligence and Linguistics, 5(3), 89-97.
Ismailova, Z. (2020). Neural machine translation for Uzbek: A state-of-the-art approach. International Journal of Computational Linguistics, 17(2), 213-225.
Kornai, A. (2008). Formalizing agglutination: Challenges for Turkic machine translation. Proceedings of the International Conference on Computational Linguistics (COLING 2008), 116-121.
Khamidov, S. (2021). Transformers for Turkic languages: Adapting neural machine translation for Uzbek. Journal of Neural Networks, 23(1), 76-90.
Nizamov, I. (2017). Syntactic variability in machine translation for Uzbek. Proceedings of the Central Asian Linguistics Conference, 45-59.
Papineni, K., Roukos, S., Ward, T., & Zhu, W. J. (2002). BLEU: A method for automatic evaluation of machine translation. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), 311-318. https://doi.org/10.3115/1073083.1073135
Rei, M., Tiedemann, J., & Haverinen, H. (2020). COMET: A neural evaluation metric for machine translation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 5757-5768. https://doi.org/10.18653/v1/2020.acl-main.513
Safarov, D. (2019). Neural machine translation for Uzbek: Challenges and solutions. Journal of Machine Translation Research, 8(3), 12-24.
Sennrich, R., & Haddow, B. (2016). Neural machine translation. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), 86-96.
Sharoff, S. (2018). Evaluating machine translation for low-resource Turkic languages. Journal of Machine Translation, 32(2), 115-135. https://doi.org/10.1007/s10590-018-9200-x
Talibov, I (2020). Development of neural machine translation systems for Uzbek. Tashkent, Uzbekistan: Tashkent State University Press.
Tursunov, A. (2021). Improving machine translation for Uzbek using neural networks. Computational Linguistics and Natural Language Processing, 18(4), 71-83.
Tadjibaeva, R. (2020). The role of domain-specific corpora in enhancing Uzbek machine translation. Central Asian Computational Linguistics Journal, 7(2), 98-110.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998-6008. https://doi.org/10.48550/arXiv.1706.03762
Vilar, D., Rojas, A., & Casacuberta, F. (2006). A comprehensive evaluation of statistical machine translation for low-resource languages. Proceedings of the 11th Annual Conference of the European Association for Machine Translation (EAMT 2006), 58-67.
Way, A., & Toral, A. (2018). What level of quality can neural machine translation attain on a new language pair? Machine Translation, 32(3), 141-159. https://doi.org/10.1007/s10590-018-9201-9