@inproceedings{signoroni-etal-2026-rebelot,
    title = "Rebel{\`o}t: Datasets and Token-Level Language Identification for {L}ombard-{I}talian-{E}nglish Code-Mixing",
    author = "Signoroni, Edoardo  and
      Bedna{\v{r}}{\'i}kov{\'a}, Emma  and
      Rychly, Pavel",
    editor = "Ojha, Atul Kr.  and
      Sakti, Sakriani  and
      Soria, Claudia  and
      Melero, Maite  and
      McCrae, John P.  and
      Lignos, Constantine  and
      Liu, Chao-Hong  and
      Claramunt, German Rigau  and
      Rehm, Georg",
    booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
    month = may,
    year = "2026",
    address = "Palma, Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.sigul-1.25/",
    doi = "10.63317/4yids37agyxu",
    pages = "253--262",
    abstract = "Lombard is an endangered and under-resourced Gallo-Italic language variety that exists with Standard Italian. As with other language varieties of Italy, code-switching and code-mixing is common between Lombard and Italian in everyday conversation and with English, online. This linguistic complexity, and the lack of a unified written standard, poses challenges for Natural Language Processing tools. We introduce Rebel{\`o}t, a novel multi-domain, token-level annotated dataset for Lombard-Italian-English code-mixing. Furthermore, we develop and evaluate three variants of a token-level Language Identification (LID) tool based on a pre-trained encoder architecture, fine-tuned using both authentic data from our corpus and synthetically generated code-mixed text. Our evaluation demonstrates that the optimal model variant achieves an accuracy of over 0.99 on token-level prediction, and substantially outperforms widely used off-the-shelf LID baselines at sentence-level."
}