Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection. (arXiv:2210.16539v1 [cs.CL])

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating
preventive care and to delay further progression. Speech based automatic AD
screening systems provide a non-intrusive and more scalable alternative to
other clinical screening techniques. Textual embedding features produced by
pre-trained language models (PLMs) such as BERT are widely used in such
systems. However, PLM domain fine-tuning is commonly based on the masked word
or sentence prediction costs that are inconsistent with the back-end AD
detection task. To this end, …

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