@inproceedings{sawkar-etal-2025-mcmaster,
    title = "{M}c{M}aster at {L}e{W}i{D}i-2025: Demographic-Aware {R}o{BERT}a",
    author = "Sanghani, Aadi  and
      Azadi, Sarvin  and
      Jethra, Virendra  and
      Welch, Charles",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Frenda, Simona  and
      Tonelli, Sara  and
      Dudy, Shiran",
    booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/bulk-corrections-2025-11-25/2025.nlperspectives-1.18/",
    doi = "10.18653/v1/2025.nlperspectives-1.18",
    pages = "208--218",
    ISBN = "979-8-89176-350-0",
    abstract = "We present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition."
}