  Understanding robustness and sensitivity of BERT models predicting
Alzheimer's disease from text is important for both developing better
classification models and for understanding their capabilities and limitations.
In this paper, we analyze how a controlled amount of desired and undesired text
alterations impacts performance of BERT. We show that BERT is robust to natural
linguistic variations in text. On the other hand, we show that BERT is not
sensitive to removing clinically important information from text.
