The role of translation workflows in overcoming translation difficulties : a comparative analysis of human and machine translation (post-editing) approaches

This study investigates the impact of different translation workflows and underlying machine translation technologies on the translation strategies used in literary translations. We compare human translation, translation within a computer-assisted translation (CAT) tool, and machine translation post-editing (MTPE), alongside neural machine translation (NMT) and large language models (LLMs). Using three short stories translated from English into Dutch, we annotated translation difficulties and strategies employed to overcome them. Our analysis reveals differences in translation solutions across modalities, highlighting the influence of technology on the final translation. The findings suggest that while MTPE tends to produce more literal translations, human translators and CAT tools exhibit greater creativity and employ more non-literal translation strategies. Additionally, LLMs reduced the number of literal translation solutions compared to traditional NMT systems. While our study provides valuable insights, it is limited by the use of only three texts and a single language pair. Further research is needed to explore these dynamics across a broader range of texts and languages, to better understand the full impact of translation workflows and technologies on literary translation.