@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://aclanthology.org/2025.acl-long.514/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
%A Sheng, Zhecheng
%A Ding, Xiruo
%A Hur, Brian
%A Li, Changye
%A Cohen, Trevor
%A Pakhomov, Serguei V. S.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F sheng-etal-2025-mitigating
%X 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.
%R 10.18653/v1/2025.acl-long.514
%U https://aclanthology.org/2025.acl-long.514/
%U https://doi.org/10.18653/v1/2025.acl-long.514
%P 10419-10434
Markdown (Informal)
[Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking](https://aclanthology.org/2025.acl-long.514/) (Sheng et al., ACL 2025)
ACL