Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators
Yanxia Yang, Runze Liu, Xingmin Qian, Jiayue Ni
Abstract
Abstract Machine translation has become a popular option for news circulation, due to its speed, cost-effectiveness and improving quality. However, it still remains uncertain whether machine translation is effective in helping novice translators in news translation. To investigate the effectiveness of machine translation, this study conducted a Chinese-English news translation test to compare the performance and perception of translation learners in machine translation post-editing and manual translation. The findings suggest that it is challenging for machine translation to understand cultural and semantic nuances in the source language, and produce coherent structural translation in the target language. No significant quality difference was observed in post-editing and manual translation, though post-editing quality was found to be slightly better. Machine translation can help to reduce translation learners’ processing time and mental workload. Compared to manual translation, machine translation post-editing is considered as a preferred approach by translation learners in news translation. It is hoped that this study could cast light on the integration of machine translation into translator training programs.