A New Challenge in Translator Training: Machine Translation Post-editing

The need to integrate machine translation post-editing (MTPE) in translator training reflects the increasing presence of advanced technologies in the translation industry. This study examines the role and significance of MTPE instruction in modern translator training, emphasizing its contribution to improving translation efficiency, quality, and consistency. It was observed that translation students in our programme frequently utilize machine translation tools, such as Google Translate, DeepL, or ChatGPT, often without fully understanding their limitations. We assume that incorporating more language-pair-specific MTPE practice into the curriculum could enhance students’ translation performance. To investigate this, we evaluated the ability of graduate translation students at Sapientia Hungarian University of Transylvania to translate texts from English into Hungarian using MTPE. The study involved analysing translations of two texts translated by 18 students to identify common errors, including issues related to grammar and syntax, meaning inaccuracies, omissions or additions, and overly literal translations. Furthermore, a post-task interview was conducted to gain deeper insights into our students’ strategies and perceived challenges. Based on these findings, we aim to update the curriculum by incorporating targeted theoretical and practical components related to MTPE.