@inproceedings{tahaei-bergler-2025-demographic,
    title = "Demographic Features for Annotation-Aware Classification",
    author = "Tahaei, Narjes  and
      Bergler, Sabine",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.142/",
    pages = "1232--1236",
    abstract = "This paper revisits the use of annotator demographics as interpretable meta-information for modeling such variation. We adapt a lightweight attention mechanism, Annotation-Wise Attention Network (AWAN), to condition predictions on demographic features, enabling per-annotator modeling. Experiments on the EXIST sexism dataset show that AWAN improves classification performance over standard baselines, especially in cases of high annotator disagreement."
}