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.

The use of machine translation is increasingly being explored for the translation of literary texts, but there is still a lot of uncertainty about the optimal translation workflow in these scenarios. While overall quality is quite good, certain textual characteristics can be different in a human translated text and a text produced by means of machine translation post-editing, which has been shown to potentially have an impact on reader perceptions and experience as well. In this study, we look at textual characteristics from short story translations from B.J. Novak’s One more thing into Dutch. Twenty-three professional literary translators translated three short stories, in three different conditions: using Word, using the classic CAT tool Trados, and using a machine translation post-editing platform specifically designed for literary translation. We look at overall text characteristics (sentence length, type-token ratio, stylistic differences) to establish whether translation workflow has an impact on these features, and whether the three workflows lead to very different final translations or not.

DUAL-T is an EU-funded project which aims at involving literary translators in the testing of technology-inclusive workflows. Participants will be asked to translate three short stories using, respectively, (1) a text editor combined with online resources, (2) a Computer-Aided Translation (CAT) tool, and (3) a Machine Translation Post-editing (MTPE) tool.