Papers with dementia detection
Detecting dementia in Mandarin Chinese using transfer learning from a parallel corpus (N19-1)
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| Challenge: | Existing methods for automatic detection of Alzheimer's disease (AD) are limited by a lack of data. |
| Approach: | They propose a method to learn a correspondence between independently engineered lexicosyntactic features in two languages, using a large parallel corpus of out-of-domain movie dialogue data. |
| Outcome: | The proposed method outperforms both unilingual and machine translation-based baselines in Mandarin Chinese and is the first to transfer feature domains in detecting cognitive decline. |
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking (2025.acl-long)
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| Challenge: | Pre-trained neural language models fine-tuned on AD transcripts perform well, but little research has explored the effects of the gender of the speakers represented by these transcripts. |
| Approach: | They propose to use the Extended Confounding Filter and the Dual Filter to isolate and ablate weights associated with gender in dementia datasets. |
| Outcome: | The proposed methods overfit to training data distributions and disrupt gender-related weights, with the trade-off of slightly reduced dementia detection performance. |
Towards Domain-Agnostic and Domain-Adaptive Dementia Detection from Spoken Language (2023.acl-long)
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| Challenge: | Domain adaptation (DA) techniques have been used to improve performance of NLP systems for healthcare tasks due to numerous complexities of data. |
| Approach: | They propose to use domain adaptation techniques to improve generalizability across diverse datasets for dementia detection. |
| Outcome: | The proposed model achieves a 22% increase in accuracy adapting from a conversational to task-oriented dataset compared to a jointly trained baseline. |
Adversarial Text Generation using Large Language Models for Dementia Detection (2024.emnlp-main)
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| Challenge: | Large language models excel in text classification tasks, but they do not perform well with picture description. |
| Approach: | They propose an interpretable classification approach by Adversarial Text Generation (ATG) that could relate dementia detection with other tasks. |
| Outcome: | The proposed approach achieves 85% accuracy, >10% improvement over the previous methods. |