@inproceedings{dei-2026-detecting-reported,
    title = "Detecting reported speech as a token classification task: an application to Classical {L}atin?",
    author = "Dei, Agustin",
    editor = "Alves, Diego  and
      Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Pagel, Janis  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.latechclfl-1.24/",
    pages = "251--256",
    ISBN = "979-8-89176-373-9",
    abstract = "This paper presents the first application of an automatic token-classification approach for detecting reported speech spans in Classical Latin using transformer-based neural architectures.Focusing on Seneca the Elder{'}s Declamatory Anthology, the study addresses the text{'}s highly polyphonic nature, resulting from theuse of reported speech. Instead of relying exclusively on sentence-level syntactic information, the proposed approach treats reported speech detection as a token-level sequence labeling problem. This enables the identification of reported speech spans extending across multiple sentences. We fine-tune three Latin neural language models {---}LatinBERT, LaBERTa, and PhilBERTa{---} for binary token-level classification and conduct experiments both with and without punctuation. The results show that RoBERTa-based models effectively identify reported speech, with LaBERTa achieving the best performance (F1 scores above 0.90)."
}