@inproceedings{dinu-etal-2024-itgrasyll,
    title = "{I}t{G}ra{S}yll: A Computational Analysis of Graphical Syllabification and Stress Assignment in {I}talian",
    author = "Dinu, Liviu  and
      Iordache, Ioan-Bogdan  and
      Georgescu, Simona  and
      Cristea, Alina Maria  and
      Guita, Bianca",
    editor = "Dell'Orletta, Felice  and
      Lenci, Alessandro  and
      Montemagni, Simonetta  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
    month = dec,
    year = "2024",
    address = "Pisa, Italy",
    publisher = "CEUR Workshop Proceedings",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.clicit-1.38/",
    pages = "316--324",
    ISBN = "979-12-210-7060-6",
    abstract = "In this paper we build a dataset of Italian syllables. We perform quantitative and qualitative analyses on the syllabification and stress assignment in Italian. We propose a machine learning model, based on deep-learning techniques, for automatically inferring syllabification and stress assignment. For stress prediction we report 94.45{\%} word-level accuracy, and for syllabification we report 98.41{\%} word-level accuracy and 99.82{\%} hyphen-level accuracy."
}