  Fine-tuned Bidirectional Encoder Representations from Transformers
(BERT)-based sequence classification models have proven to be effective for
detecting Alzheimer's Disease (AD) from transcripts of human speech. However,
previous research shows it is possible to improve BERT's performance on various
tasks by augmenting the model with additional information. In this work, we use
probing tasks as introspection techniques to identify linguistic information
not well-represented in various layers of BERT, but important for the AD
detection task. We supplement these linguistic features in which
representations from BERT are found to be insufficient with hand-crafted
features externally, and show that jointly fine-tuning BERT in combination with
these features improves the performance of AD classification by upto 5\% over
fine-tuned BERT alone.
