  In this work, we propose three explainable deep learning architectures to
automatically detect patients with Alzheimer`s disease based on their language
abilities. The architectures use: (1) only the part-of-speech features; (2)
only language embedding features and (3) both of these feature classes via a
unified architecture. We use self-attention mechanisms and interpretable
1-dimensional ConvolutionalNeural Network (CNN) to generate two types of
explanations of the model`s action: intra-class explanation and inter-class
explanation. The inter-class explanation captures the relative importance of
each of the different features in that class, while the inter-class explanation
captures the relative importance between the classes. Note that although we
have considered two classes of features in this paper, the architecture is
easily expandable to more classes because of its modularity. Extensive
experimentation and comparison with several recent models show that our method
outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the
DementiaBank dataset while being able to generate explanations. We show by
examples, how to generate these explanations using attention values.
