A Comparative Study on the Quality Assessment of Human and Machine English-Chinese Translation and Pathways to Improve Quality in Translation Mode of Human-Machine Collaboration

The rapid advancement of artificial intelligence technology has significantly enhanced the efficiency and quality of machine translation. However, machine translation still exhibits limitations in current translation practice. The study selects both student and machine translations of two texts from the final examination of a third-year translation course for English majors at a university. Focusing on a quality assessment of human and machine E-C translations, it conducts a comparative analysis of the quality differences between the two, delves into the causes of errors in student translations, and explores how English majors can effectively utilize machine translation technology in the AI era to improve their translation competence and quality, while also proposing innovative approaches for translation teaching reform. The findings reveal that both human and machine translation have their respective strengths and weaknesses, and students need to adapt to a human-machine collaborative mode by using machine translation critically. The study aims to provide insights for the cultivation of English majors and their learning processes, thereby contributing to the enhancement of talent development, improving students’ translation efficiency and quality, and fostering their growth into highly qualified translation professionals who meet the industry demands of the digital and intelligent era.