Evaluation of German-Slovak AI Translation of Stock Market News) - Filip Kalaš (Linguistische Treffen in Wrocław)
The rapid advancement of technology has transformed communication, particularly through innovations in language and translation technologies. These tools have become essential for global interactions and are pivotal in modern linguistic studies. This paper investigates the application of three online statistical machine translation tools, ChatGPT-4, Google Translate and DeepL, for translating specialised German texts into Slovak. The study focuses on ten articles discussing various aspects of the stock exchange, a domain characterised by complex terminology and contextual nuances. By employing both quantitative and qualitative methods, the research evaluates the error rates, translation effectiveness, and the accuracy of these tools in preserving the original context. Specific challenges addressed include handling linguistic intricacies, domain-specific terminologies, and contextual fidelity unique to stock exchange texts. The analysis combines error rate calculations with qualitative assessments, offering a comprehensive evaluation of the tools’ capabilities. The findings underscore the limitations and strengths of automated translation systems when applied to specialised text genres, providing critical insights for developers and practitioners in translation technology. The study shows that the tools often struggle with compound terms, anglicisms and jargon words. This study contributes to the growing body of knowledge in language technology, specialised domain translation, and machine translation research, highlighting areas for improvement and potential advancements in automated systems. Its practical implications extend to linguists, translators, and software developers aiming to enhance machine translation tools for specialised applications.