@inproceedings{moerman-etal-2025-tailoring,
    title = "Tailoring Machine Translation for Scientific Literature through Topic Filtering and Fuzzy Match Augmentation",
    author = "Moerman, Thomas  and
      Vanallemeersch, Tom  and
      Szoc, Sara  and
      Tezcan, Arda",
    editor = "Tsunakawa, Takashi  and
      Sudoh, Katsuhito  and
      Goto, Isao",
    booktitle = "Proceedings of the Eleventh Workshop on Patent and Scientific Literature Translation (PSLT 2025)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.pslt-1.2/",
    pages = "13--26",
    ISBN = "978-2-9701897-2-5",
    abstract = "To enhance the accessibility of scientific literature in multiple languages and facilitate the exchange of information among scholars and a wider audience, there is a need for high-performing specialized machine translation (MT) engines. However, this requires efficient filtering and the use of domain-specific data. In this study, we investigate whether approaches for increasing training data using topic filtering and more efficient use of such data through exploiting fuzzy matches (i.e. similar translations to a given input; FMs) improve translation quality. We apply these techniques both to sequence-to-sequence MT models and off-the-shelf multilingual large language models (LLMs) in three scientific disciplines. Our results suggest that the combination of topic filtering and FM augmentation is an effective strategy for training neural machine translation (NMT) models from scratch, not only surpassing baseline NMT models but also delivering improved translation performance compared to smaller LLMs in terms of the number of parameters. Furthermore, we find that although FM augmentation through in-context learning generally improves LLM translation performance, limited domain-specific datasets can yield results comparable to those achieved with additional multi-domain datasets."
}