TERMINOLOGY AS A LEARNING OBJECT IN MACHINE TRANSLATION
Keywords:
Terminology, Machine Translation, Learning Object, Translation Quality, Contextual UnderstandingAbstract
Machine translation (MT) has seen significant advancements in recent years, yet challenges persist in accurately translating specialized terminology across various domains.
This paper explores the role of terminology as a learning object in machine translation. It examines the significance of terminology in ensuring translation accuracy, consistency, and contextual understanding. Through a comprehensive review of literature and empirical evidence, this article highlights the importance of incorporating terminology into MT models as a learning object to enhance translation quality.
References
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.
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.
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).
Aripov, M., Sharipbay, A., Abdurakhmonova, N., Razakhova B.: Ontology of grammar rules as example of noun of Uzbek and Kazakh languages. In: Abstract of the VI International Conference “Modern Problems of Applied Mathematics and Information Technology - Al-Khorezmiy 2018”, pp. 37–38, Tashkent, Uzbekistan (2018)
Bentivogli, L., Bisazza, A., & Cettolo, M. (2016). Neural versus phrase-based machine translation quality: a case study. arXiv preprint arXiv:1608.04631.
Cao, J., Chen, K., Jin, Y., & Li, X. (2020). A Review of Machine Translation Technology Based on Deep Learning. In Advances in Intelligent Systems and Computing (Vol. 1131, pp. 587-595). Springer.
Chatterjee, S., & Gupta, K. (2018). Sentiment Analysis of Amazon Product Reviews Using Machine Learning Algorithms. Procedia Computer Science, 132, 1078-1085.
Chu, C., Wang, Y., Wu, W., & Ma, W. (2018). Domain Adaptation for Neural Machine Translation with Domain-Invariant Feature Embeddings. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1743-1755).
Koehn, P. (2017). Neural machine translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 9-12).
Pinto, M., Nisi, V., & Palmonari, M. (2018). A methodology for the construction of multilingual domain-specific terminologies: the case of environment. Data Science Journal, 17(0), 13.
Popović, M., & Ney, H. (2018). Towards language adaptive machine translation. Machine Translation, 32(3), 225-253.
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, 30, 5998-6008.
