PARALLEL KORPUS ASOSIDA MASHINA TARJIMASIDA KONTEKSTGA BOG‘LIQ BIRLIKLAR TAHLILI (PARATRANSLATOR.UZ PLATFORMASI MISOLIDA)
##article.subject##:
parallel korpus, mashina tarjimasi, kontekst, korpus lingvistikasi, Paratranslator, kompyuter lingvistikasi##article.abstract##
Maqolada kontekstga bog‘liq til birliklarini parallel korpus asosida mashina tarjimasida tahlil qilish xususiyatlari ko‘rib chiqilgan. Empirik material sifatida kontekstga yo‘naltirilgan tarjima texnologiyasi asosida ishlab chiqilgan ko‘p tilli Paratranslator.uz platformasidan foydalanilgan. Tadqiqot shuni ko‘rsatadiki, kontekstni hisobga olish ko‘p ma’noli so‘zlar, kollokatsiyalar va frazeologik birliklarni yanada aniqroq talqin qilish imkonini beradi. Parallel kontekstlar asosida kommunikativ muhitga qarab tarjima yechimlaridagi farqlar aniqlangan. Natijalar korpusga yo‘naltirilgan yondashuv mashina tarjimasi sifati hamda uning kommunikativ adekvatligini oshirishga xizmat qilishini ko’rsatadi.
Библиографические ссылки
P. F. Brown, S. A. Della Pietra, V. J. Della Pietra, R. L. Mercer, “The mathematics of statistical machine translation: Parameter estimation,” Comput. Linguist., vol. 19, no. 2, pp. 263–311, 1993.
F. J. Och, H. Ney, “A systematic comparison of various statistical alignment models,” Comput. Linguist., vol. 29, no. 1, pp. 19–51, 2003.
S. Johansson, Seeing through multilingual corpora: On the use of corpora in contrastive studies. Amsterdam: John Benjamins, 2007.
D. Bahdanau, K. Cho, Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, 2015.
A. Vaswani et al., “Attention is all you need,” in Adv. Neural Inf. Process. Syst., vol. 30, 2017.
C.Hardmeier, Discourse in Statistical Machine Translation, Ph.D. dissertation, Uppsala Univ., 2012.
Z. Tu, Y. Liu, L. Shang, and Z. Liu, “Learning to remember translation history with a continuous cache,” Trans. Assoc. Comput. Linguist., vol.6, pp. 407–420, 2018.
L. Venuti, The Translator’s Invisibility. London, U.K.: Routledge, 1995.
M. Baker, In Other Words: A Coursebook on Translation. London, U.K.: Routledge, 2011.
L. Miculicich, D. Ram, N. Pappas, J. Henderson, “Document-level neural machine translation with hierarchical attention networks,” in Proc. EMNLP, Brussels, Belgium, Oct. 2018, pp. 2947–2953.
P. Zhang et al., “Learning contextualized sentence representations for document-level neural machine translation,” arXiv preprint arXiv:2003.13205, 2020.
J. Barwise and J. Perry, Situations and Attitudes. Cambridge, MA, USA: MIT Press, 1983.
J. Tiedemann, Bitext Alignment, Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, 2011.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in Proc. Int. Conf. on Learning Representations (ICLR), 2015. https://arxiv.org/abs/1409.0473
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019.
Abdurakhmonova N., Shamsiyeva G. Context-Based Multilingual Translation Technology: on the Example of the Paratranslator Platform. In: Proceedings of the 10th International Conference on Computer Science and Engineering (IEEE UBMK’25), Istanbul, Türkiye, 2025, pp. 1800–1804.
N. A. Zaynobiddin qizi and S. G. Asliddin qizi, “Theoretical Foundations of Corpus-based Uzbek-English Machine Translation,” 2024 IEEE 3rd International Conference on Problems of Informatics, Electronics and Radio Engineering (PIERE), Novosibirsk, Russian Federation, 2024, pp. 1650-1653, doi: 10.1109/PIERE62470.2024.10805010.
N.Abdurakhmonova, I. Alisher, and G.Toirova, “Applying Web Crawler Technologies for Compiling Parallel Corpora as one Stage of Natural Language Processing,” 2022 7th International Conference on Computer Science and Engineering (UBMK), Diyarbakir, Turkey, 2022, pp. 73-75, doi: 10.1109/UBMK55850.2022.9919521.