Machine Translation Implementation in Automatic Subtitling from a Subtitlers' Perspective

In recent years, automatic subtitling has gained considerable scholarly attention. Implementing machine translation in subtitling editors faces challenges, being a primary process in automatic subtitling. Therefore, there is still a significant research gap when it comes to machine translation implementation in automatic subtitling. This project compared different levels of non-verbal input videos from English to Chinese Simplified to examine post-editing efforts in automatic subtitling. The research collected the following data: process logs, which records the total time spent on the subtitles, keystrokes, and user experience questionnaire (UEQ). 12 subtitlers from a translation agency in Mainland China were invited to complete the task. The results show that there are no significant differences between videos with low and high levels of nonverbal input in terms of time spent. Furthermore, the subtitlers spent more effort on revising spotting and segmentation than translation when they post-edited texts with a high level of nonverbal input. While a majority of subtitlers show a positive attitude towards the application of machine translation, their apprehension lies in the potential overreliance on its usage.