@inproceedings{sanchez-martinez-etal-2024-findings,
    title = "Findings of the {WMT} 2024 Shared Task Translation into Low-Resource Languages of {S}pain: Blending Rule-Based and Neural Systems",
    author = "S{\'a}nchez-Mart{\'i}nez, Felipe  and
      Perez-Ortiz, Juan Antonio  and
      Galiano Jimenez, Aaron  and
      Oliver, Antoni",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.wmt-1.57/",
    doi = "10.18653/v1/2024.wmt-1.57",
    pages = "684--698",
    abstract = "This paper presents the results of the Ninth Conference on Machine Translation (WMT24) Shared Task {\textquotedblleft}Translation into Low-Resource Languages of Spain{\textquotedblright}'. The task focused on the development of machine translation systems for three language pairs: Spanish-Aragonese, Spanish-Aranese, and Spanish-Asturian. 17 teams participated in the shared task with a total of 87 submissions. The baseline system for all language pairs was Apertium, a rule-based machine translation system that still performs competitively well, even in an era dominated by more advanced non-symbolic approaches. We report and discuss the results of the submitted systems, highlighting the strengths of both neural and rule-based approaches."
}