Comparing human and machine translation: qualitative criteria

Abstract

The rapid advancement of machine translation (MT) has sparked ongoing debate about its effectiveness compared to human translation (HT). While MT systems such as Google Translate and DeepL have improved significantly, they continue to face challenges related to accuracy, cultural nuance, and stylistic appropriateness. This paper explores the qualitative criteria used to evaluate translations, comparing human and machine outputs in terms of fluency, accuracy, cultural adaptation, and contextual understanding. Through an analysis of examples drawn from legal contracts, marketing materials, and corporate communications, the study highlights the respective strengths and limitations of both approaches, offering insights into scenarios where human expertise remains indispensable.