This paper investigates how post-editors of literary texts react and respond to the way metaphors have been translated by Neural Machine Translation (NMT) and Large Language Models (LLM). The results show that one in three metaphors in the output were changed by the posteditors, demonstrating that the translation of figurative language is indeed problematic in literary MT (LitMT). The responses indicate that the post-editors were aware of overly literal translations, though mostly for multiword expressions. Moreover, at times they found it difficult to determine whether solutions were acceptable. They rated the overall quality of the MT output as quite poor and stated that the post-editing was more work and more effort than it would have been translating from scratch. This supports previous studies arguing that post-editing constrains translators in their creativity and diminishes their sense of text ownership.

This paper explores how students conceptualise the processes involved in human and machine translation, and how they describe the similarities and differences between them. The paper presents the results of a survey involving university students (B.A. and M.A.) taking a course on translation who filled out an online questionnaire distributed in Finnish, Dutch and English. Our study finds that students often describe both human translation and machine translation in similar terms, suggesting they do not sufficiently distinguish between them and do not fully understand how machine translation works. The current study suggests that training in Machine Translation Literacy may need to focus more on the conceptualisations involved and how conceptual and vernacular misconceptions may affect how translators understand human and machine translation.