@inproceedings{lerner-yvon-2025-towards,
    title = "Towards the Machine Translation of Scientific Neologisms",
    author = "Lerner, Paul  and
      Yvon, Fran{\c{c}}ois",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.63/",
    pages = "947--963",
    abstract = "Scientific research continually discovers and invents new concepts, which are then referred to by new terms, neologisms, or neonyms in this context. As the vast majority of publications are written in English, disseminating this new knowledge to the general public often requires translating these terms. However, by definition, no parallel data exist to provide such translations. Therefore, we propose to leverage term definitions as a useful source of information for the translation process. As we discuss, Large Language Models are well suited for this task and can benefit from in-context learning with co-hyponyms and terms sharing the same derivation paradigm. These models, however, are sensitive to the superficial and morphological similarity between source and target terms. Their predictions are also impacted by subword tokenization, especially for prefixed terms."
}