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.

The translation of metaphorical language presents a challenge in Natural Language Processing as a result of its complexity and variability in terms of linguistic forms, communicative functions, and cultural embeddedness. This paper investigates the performance of different state-of-the-art Machine Translation (MT) systems and Large Language Models (LLMs) in metaphor translation in literary texts (English->Dutch), examining how metaphorical language is handled by the systems and the types of errors identified by human evaluators. While commercial MT systems perform better in terms of translation quality based on automatic metrics, the human evaluation demonstrates that open-source, literary-adapted NMT systems translate metaphors equally accurately. Still, the accuracy of metaphor translation ranges between 64-80%, with lexical and meaning errors being the most prominent. Our findings indicate that metaphors remain a challenge for MT systems and adaptation to the literary domain is crucial for improving metaphor translation in literary texts.

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.