@inproceedings{macias-etal-2024-scaling,
    title = "Scaling Sustainable Development Goal Predictions across Languages: From {E}nglish to {F}innish",
    author = {Macias, Melany  and
      Kharlashkin,, Lev  and
      Huovinen, Leo  and
      H{\"a}m{\"a}l{\"a}inen, Mika},
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      Pirinen, Flammie  and
      Macias, Melany  and
      Crespo Avila, Mario},
    booktitle = "Proceedings of the 9th International Workshop on Computational Linguistics for Uralic Languages",
    month = nov,
    year = "2024",
    address = "Helsinki, Finland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.iwclul-1.17/",
    pages = "132--137",
    abstract = "In this paper, we leverage an exclusive English dataset to train diverse multilingual classifiers, investigating their efficacy in adapting to Finnish data. We employ an exclusively English classification dataset of UN Sustainable Development Goals (SDG) in an education context, to train various multilingual classifiers and examine how well these models can adapt to recognizing the same classes within Finnish university course descriptions. It`s worth noting that Finnish, with a mere 5 million native speakers, presents a significantly less-resourced linguistic context compared to English. The best performing model in our experiments was mBART with an F1-score of 0.843."
}