the evaluation of AI-generated subtitles

Abstract

AI in translation studies is a relatively new field that has piqued the interest of researchers due to its potentials on the translation as practice and a field. Following a review of subtitle evaluation and AI within the field of translation studies, this dissertation explores the domain of AI-generated subtitles and assesses their quality in comparison to human-generated subtitles. Through manual comparative analyses and utilizing quality assessment models FAR and MQM, the research uncovers the strengths and weaknesses of AI-generated subtitles, shedding light on their struggles with nuanced language use and cultural references. Furthermore, the research underscores the paramount importance of cultural and contextual sensitivity in subtitling, an area where human subtitlers excel. It highlights the practical implications of these findings for translation practices and education, advocating for a balanced approach that harnesses the strengths of both AI and human capabilities. Despite certain limitations, including a limited sample size and Netflix-specific focus, the distraction illuminates the dynamic landscape of subtitling, emphasizing the potential for AI-human collaboration to optimize practices and ensure the delivery of high-quality subtitles.