SPECIFIC FEATURES OF MACHINE TRANSLATION FROM ENGLISH TO UKRAINIAN

The article examines the results of machine translation from English to Ukrainian, which was made by three machine translation systems and two artificial intelligence systems. The intense development of computer technologies has led to scientific disciplines that are between linguistics and computer science, such as machine translation. Modern machine translation systems are based on the neural method, although some of them were developed on the statistical method. Artificial intelligence started developing since the middle of the past century and has reached it’s highest point nowadays, which includes various systems. Machine translation is also one of the various functions of artificial intelligence systems, which is made based on the neural method, imitating human intuition. The analysis of machine translation results is based on a text from the field of sociolinguistics on the topic of bilingualism which is relevant due to the globalization processes in the modern world and the existence of countries with two or more official languages or one official language but with the parallel use of other languages or dialects, people interact with different languages so it is necessary to ensure effective interlingual communication, so this topic is actively studied. Translation of scientific texts written in English are relevant for translation due to the fact that Ukrainian scientists participate in foreign conferences and some of them are not fluent in Englsih also works by foreign scientists are being published in Ukrainian journals mostly in English. Texts that are based on the topic of bilingual countries and societies analyze the language situation, give the results in quantitative terms, conclude and give suggestions on the language problem solving so they are relevant for translation. The text was translated with the following machine translation systems – Google Translate, DeepL Translate, Glosbe Translate; and with the following artificial intelligence systems – ChatGPT, Gemini. The analysis of the results of equivalent machine translation variants was made using the following online bilingual dictionaries – Glosbe, Lingea; and the following online explanatory dictionaries of the source and target languages: Ukrainian – SLOVNYK.ua; English – Cambridge Dictionary, Oxford Learner’s Dictionary. The article focuses on the equivalents which are obtained from the systems, identifying relevant and problematic translation variants using online dictionaries. The aim of this article is to select the system which is more optimal to use in translation practice by a comparative analysis of the machine translation results of both with the original and with other systems’ translation and to indicate relevant and problematic translation variants in quantitative terms.