ANALYSIS OF CONTEXT-DEPENDENT UNITS IN MACHINE TRANSLATION BASED ON A PARALLEL CORPUS (A CASE STUDY OF THE PARATRANSLATOR.UZ PLATFORM)
Keywords:
parallel corpus, machine translation, context, corpus linguistics, Paratranslator, computational linguistics.Abstract
This article examines the features of analyzing context-dependent linguistic units in machine translation based on a parallel corpus. The multilingual platform Paratranslator.uz, developed using context-oriented translation technology, is employed as the empirical basis of the study. The research demonstrates that taking context into account enables more accurate interpretation of polysemous words, collocations, and phraseological units. Based on parallel contexts, differences in translation choices are identified depending on the communicative environment. The findings confirm that a corpus-based approach contributes to improving the quality of machine translation and its communicative adequacy.
References
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.
