Automatic Machine Translation: A Study of Translation Quality, User Habits, Competence, and Decision-Making

This dissertation investigates the quality of machine translation (MT), examines the state of the art in translation technology, and explores the conscious and strategic use of automated machine translation (AMT) tools in professional and academic contexts. The rapid evolution of MT systems, fueled by advancements in neural machine translation (NMT), has significantly enhanced their accuracy and fluency. However, challenges persist, particularly in the translation of idiomatic expressions, domain-specific terminology, and cultural nuances. Through an extensive review of the current literature and empirical evaluation of MT systems, this study assesses their performance across various languages and contexts, identifying strengths, weaknesses, and areas for improvement.