@inproceedings{gibert-etal-2025-mind,
    title = "Mind the Gap: {Diverse} {NMT} Models for Resource-Constrained Environments",
    author = {Gibert, Ona de  and
      O{'}Brien, Dayy{\'a}n  and
      Vari{\v{s}}, Du{\v{s}}an  and
      Tiedemann, J{\"o}rg},
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.nodalida-1.21/",
    pages = "209--216",
    ISBN = "978-9908-53-109-0",
    abstract = "We present fast Neural Machine Translation models for 17 diverse languages, developed using Sequence-level Knowledge Distillation. Our selected languages span multiple language families and scripts, including low-resource languages. The distilled models achieve comparable performance while being 10x times faster than transformer-base and 35x times faster than transformer-big architectures. Our experiments reveal that teacher model quality and capacity strongly influence the distillation success, as well as the language script. We also explore the effectiveness of multilingual students. We release publicly our code and models in our Github repository: anonymised."
}