Experimental study of the impact of using hybride machine translation engines on the quality of translation of texts in the field of ornamental horticulture - Olkhovska, Alla; Levchenko, Liliya (The Journal of V.N. Karazin Kharkiv National University. Series: Foreign Philology. Methods of Foreign Language Teaching, 2023)
The article is devoted to the study of the impact of using hybrid machine translation engines (Onlinedoctranslator and DeepL Translate) on the quality of translation of texts in the field of ornamental horticulture. Within our experimental study the quality of translation was determined by the number of errors and quality of rendering horticultural terminology. Machine translation is gradually evolving, the market is extremely active and proposes many new engines uniting most promising algorithms, but it is important to determine the effectiveness of hybrid machine translation engines in translating texts of various fields, especially in the field of ornamental horticulture. In the course of conducting our research, we formulated a research hypothesis, selected a text in the field of ornamental horticulture rich in horticultural terminology and consisting of more than 30.000 printed characters without spaces, compiled a bilingual glossary based on the selected text, consisting of 200 terminological units, selected machine translation engines (Onlinedoctranslator and DeepL) and the procedure of evaluating errors, having distinguished three main types of mistakes, analyzed translated texts in terms of general quality and in terms of transferring horticultural terminology, processed the experimental data, represented the results in the form of tables providing an expert assessment of the empirical data and formulated conclusions. In the process of conducting the experiment, the hypothesis formulated at the beginning of the research was confirmed: different hybrid machine translation engines offer translations of the same text in the field of ornamental horticulture of varying quality, which we tracked by determining the overall percentage of errors in each translation performed, as well as by calculating the percentage of correct transferring of horticultural terminology. According to both indicators, DeepL showed the best results, and therefore it is advisable to recommend it for translating texts in the field of ornamental horticulture.