MACHINE TRANSLATION VS HUMAN TRANSLATION: EVALUATING ACCURACY AND EFFECTIVENESS

This article provides a comprehensive assessment of machine translation compared to human translation, with a focus on their precision and effectiveness. It investigates the development of translation techniques from initial rule-based systems and statistical machine translation to contemporary neural network models, analyzing how each approach addresses linguistic challenges. The study evaluates machine translation in terms of its speed and scalability, particularly drawing attention to recent improvements in neural machine translation that have enhanced fluency and contextual understanding. Nevertheless, it also points out shortcomings, such as challenges with idiomatic phrases, cultural subtleties, and context-specific details. On the other hand, human translation is demonstrated to thrive in interpreting intricate contexts and maintaining the original tone and intent, even though it tends to be slower and less consistent. Through a combination of quantitative error assessment and qualitative case studies, this article recommends a blended approach that combines the efficiency of machine translation with the depth of human interpretation, offering actionable suggestions for merging both methods in professional and academic environments.