Using artificial intelligence tools for translating computer terminology

Using artificial intelligence tools for translating computer terminologyMaryna Tymchyk, candidate of pedagogical sciences. Uzhhorod National University, Department of Applied Linguistics, senior [email protected], ORCID:0009-0001-9763-3378.Natalia Drabov. Uzhhorod National University, Department of Translation Theory and Practice, senior [email protected], ORCID:0000-0001-7332-6309.The emergence of artificial intelligence (AI) technologies has significantly transformed the field of translation, streamlining processes through automation and enhancing both efficiency and accuracy. As the demand for precise translation of technical texts grows, particularly in rapidly evolving fields such as computer science, AI tools have become indispensable for professional translators. This article investigates various AI-powered solutions used in translating specialised computer terminology, focusing on different approaches to machine translation, including rule-based, statistical, and neural systems.The analysis examines the translation of selected English computer terms, lexico-syntactic units, and a fragment of a specialised computer vocabulary text into Ukrainian, comparing the performance of widely used systems such as Google Translate, DeepL, and ChatGPT. Our comparative study highlights the respective strengths and limitations of different machine translation tools, emphasising their ability(or inability) to preserve technical accuracy and context when handling complex or domain-specific vocabulary.The article also describes several popular AI-based computer-assisted translation (CAT) platforms, including Trados, Smartcat, Star Transit, and DéjàVu, and outlines their specialised features designed to support translators working with technical texts.The investigation demonstrates that despite significant advances in AI translation tools, expert human revision remains essential for achieving accurate and context-sensitive translations of computer-related texts. The article concludes by discussing current challenges and identifying open questions for further research, such as improving AI’s understanding of specialised terminology, enhancing its ability to capture linguistic nuances, and optimising workflows that integrate human expertise with AI efficiency to meet the demands of computer translation in the digital age.Keywords:artificial intelligence, computer terminology translation, machine translation, statistical machine translation, neural machine translation, rule-based machine translation, CAT.