MACHINE TRANSLATION VS. HUMAN TRANSLATION: A COMPARATIVE STUDY

Abstract

Machine Translation (MT) has become a routine tool in education, business, and public services, especially after the rapid maturation of neural MT and post-editing workflows. Yet many institutions still rely on informal assumptions such as “MT is fast but inaccurate” or “human translation is always best,” which leads to weak decision-making in curriculum design, workplace communication, and quality assurance. This comparative study proposes an evaluation model that aligns translation goals with measurable outcomes, and contrasts MT and human translation across three common professional genres: technical instructions, customer-service communication, and promotional/marketing texts. Using a small pilot design with blind rating and an error-typology rubric, the study compares quality (accuracy, fluency, terminology, register, and consistency), efficiency (time, effort), and risk factors (confidentiality, accountability, and harm potential). The results indicate that MT performs competitively for predictable, terminology-driven technical content, but remains vulnerable to pragmatic meaning, politeness strategies, idioms, and brand voice. Human translation shows higher reliability for high-stakes and audience-sensitive texts, while hybrid MT + human post-editing offers the best balance of speed and quality when the task is well-scoped and supported by clear style guides. Practical recommendations are provided for teachers and administrators on how to integrate MT responsibly into lessons, assessment, and workplace-oriented language training.