@inproceedings{avila-crego-2025-systran,
    title = "{SYSTRAN} @ {IWSLT} 2025 Low-resource track",
    author = "Avila, Marko  and
      Crego, Josep",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwslt-1.33/",
    pages = "324--332",
    ISBN = "979-8-89176-272-5",
    abstract = "SYSTRAN submitted systems for one language pair in the 2025 Low-Resource Language Track. Our main contribution lies in the tight coupling and light fine-tuning of an ASR encoder (Whisper) with a neural machine translation decoder (NLLB), forming an efficient speech translation pipeline. We present the modeling strategies and optimizations implemented to build a system that, unlike large-scale end-to-end models, performs effectively under constraints of limited training data and computational resources. This approach enables the development of high-quality speech translation in low-resource settings, while ensuring both efficiency and scalability. We also conduct a comparative analysis of our proposed system against various paradigms, including a cascaded Whisper+NLLB setup and direct end-to-end fine-tuning of Whisper."
}