@inproceedings{karakanta-etal-2025-metaphors,
    title = "Metaphors in Literary Machine Translation: Close but no cigar?",
    author = "Karakanta, Alina  and
      Nas, Mayra  and
      Dorst, Aletta G.",
    editor = "Bouillon, Pierrette  and
      Gerlach, Johanna  and
      Girletti, Sabrina  and
      Volkart, Lise  and
      Rubino, Raphael  and
      Sennrich, Rico  and
      Farinha, Ana C.  and
      Gaido, Marco  and
      Daems, Joke  and
      Kenny, Dorothy  and
      Moniz, Helena  and
      Szoc, Sara",
    booktitle = "Proceedings of Machine Translation Summit XX: Volume 1",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/metadata-correction-jian-chen-ub/2025.mtsummit-1.21/",
    pages = "276--286",
    ISBN = "978-2-9701897-0-1",
    abstract = "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-{\ensuremath{>}}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."
}