@inproceedings{sheng-etal-2025-mitigating,
    title = "Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking",
    author = "Sheng, Zhecheng  and
      Ding, Xiruo  and
      Hur, Brian  and
      Li, Changye  and
      Cohen, Trevor  and
      Pakhomov, Serguei V. S.",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.514/",
    pages = "10419--10434",
    ISBN = "979-8-89176-251-0",
    abstract = "Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer{'}s disease (AD) screening. While pre-trained neural language models (LMs) fine-tuned on AD transcripts perform well, little research has explored the effects of the gender of the speakers represented by these transcripts. This work addresses gender confounding in dementia detection and proposes two methods: the Extended Confounding Filter and the Dual Filter, which isolate and ablate weights associated with gender. We evaluate these methods on dementia datasets with first-person narratives from patients with cognitive impairment and healthy controls. Our results show transformer models tend to overfit to training data distributions. Disrupting gender-related weights results in a deconfounded dementia classifier, with the trade-off of slightly reduced dementia detection performance."
}