PRACTICE-ORIENTED APPROACH TO THE FORMATION OF TRANSLATION COMPETENCE: EVALUATING THE QUALITY OF MACHINE TRANSLATION
DOI:
https://doi.org/10.37406/2521-6449/2025-2-17Keywords:
practice-oriented approach, bachelor’s degree training, machine translation, quality evaluation, translation competence, training practice, HTERAbstract
The article is devoted to the analysis of a practice-oriented approach to the formation of translation competence in the training of first (bachelor’s) level higher education students majoring in Philology. The relevance of the study is determined by the need to overcome the discrepancy between the growing demands of the labour market for machine translation techniques and approaches, and professional training of linguists and translators.The study emphasizes the importance of using a practice-oriented approach to the formation of translation competence through a system of machine translation quality assessment, which is an effective tool for training competitive specialists in the field of translation.The training practice in machine and automated translation, organised on an experimental basis, provides an organic combination of theoretical knowledge, technical skills and practical experience that meets the requirements of the modern labour market. Assessing the quality of machine translation using HTER metrics goes beyond simple technical skills and becomes a tool for developing comprehensive translation competence. Working with metrics develops linguistic, discursive and cognitive competence, as well as critical thinking, analytical skills and the ability to objectively evaluate translation decisions. Working with texts from various fields allows students to form an empirically grounded understanding of the capabilities and limitations of machine translation systems, develop the ability to anticipate problems and make informed decisions about the choice of translation strategies.A practice-oriented approach to developing translation competence through the evaluation of machine translation quality is in line with current trends in translation education, which emphasise the need for practical training in real-life conditions, collaboration and reflection. This approach contributes to the training of specialists who are able to adapt to rapid changes, critically evaluate new tools and make informed decisions.
References
Бондаренко O. C., Негрієнко М. С. Управління якістю постредагування машинного перекладу у виробничому процесі локалізації. Наукові записки. Серія «Філологічні науки». 2025. Вип. 2(213). С. 200–205. https://doi.org/10.32782/2522-4077-2025-213-27.
Гудманян А., Мишко А., Брай А. Оцінка адекватності машинного перекладу письмових спеціалізованих текстів. Advanced Linguistics. 2023. № 12. С. 67–78. https://doi.org/10.20535/2617-5339.2023.12.291604.
Калиновська І., Рогач О., Карпіна О. Інтегративний підхід до викладання усного, письмового, машинного та автоматизованого перекладу. Інноваційна педагогіка. 2025. Вип. 82. Том 1. C. 53–56. https://doi.org/10.32782/2663-6085/2025/82.1.8.
Ольховська А. С., Андрущенко Ю. Ю. Експериментальне дослідження впливу машинного перекладу на якість перекладу. Вісник Харківського національного університету імені В. Н. Каразіна. Серія «Іноземна філологія. Методика викладання іноземних мов». 2024. Вип. 99. С. 76–80. https://doi.org/10.26565/2786-5312-2024-99-09.
Рябова К. До питання про основні критерії оцінки машинного перекладу. Проблеми гуманітарних наук. Серія «Філологія» : збірник наукових праць Дрогобицького державного педагогічного університету імені Івана Франка. 2024. № 57. С. 65–71. https://doi.org/10.24919/2522-4565.2023.57.8.
Karpina O. Evaluating the quality of machine translation output with HTER in domain-specific textual environment. Лінгвістичні студії. 2023. Вип. 46. С. 85–99. https://doi.org/10.31558/1815-3070.2023.46.8.
Freitag M., et al. Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation. Transactions of the Association for Computational Linguistics. 2021. Vol. 9. P. 1460–1474. https://doi.org/10.1162/tacl_a_00437.
Naveen P., Trojovský P. Overview and Challenges of Machine Translation for Contextually Appropriate Translations. IScience. 2024. Vol. 27. Issue 10. 110878. https://doi.org/10.1016/j.isci.2024.110878.
Snover M., et al. A Study of Translation Edit Rate with Targeted Human Annotation. Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers. 2006. P. 223–231. https://aclanthology.org/2006.amta-papers.25.pdf.
Yulianto A., Supriatnaningsih R. Google Translate vs. DeepL: A Quantitative Evaluation of Close-Language Pair Translation (French to English). AJELP: Asian Journal of English Language and Pedagogy. 2021. Vol. 9(2). P. 109–127. https://doi.org/10.37134/ajelp.vol9.2.9.2021.
