When Student Translators Meet With Machine Translation: The Impacts of Machine Translation Quality and Perceived Self-Efficacy on Post-Editing Performance

Abstract

Machine translation post-editing (MTPE) is a process where humans and machines meet. While previous researchers have adopted psychological and cognitive approaches to explore the factors affecting MTPE performance, little research has been carried out to simultaneously investigate the post-editors’ cognitive traits and the post-editing task properties. This paper addresses this gap by focusing on perceived post-editing self-efficacy (PESE) as a key cognitive trait. By adopting mixed methods of keylogging, screen-recording, and subjective rating, this paper attempts to empirically assess the effects of student translators’ PESE and machine translation (MT) quality on their cognitive effort and post-edited quality. Data were obtained from 106 Chinese student translators concerning cognitive effort (indicated by processing time per word, pause density, pause duration per word, and perceived cognitive effort) of post-editing tasks and the post-edited quality (indicated by average accuracy score and average fluency score). Results show that MT quality significantly influences both the process and product within a PE task. PESE has effects on participants’ perceived cognitive effort and post-edited quality, but not on actual cognitive effort. No significant interaction effect of MT quality and PESE on PE performance was observed.