  Dialogue State Tracking (DST) models often employ intricate neural network
architectures, necessitating substantial training data, and their inference
process lacks transparency. This paper proposes a method that extracts
linguistic knowledge via an unsupervised framework and subsequently utilizes
this knowledge to augment BERT's performance and interpretability in DST tasks.
The knowledge extraction procedure is computationally economical and does not
require annotations or additional training data. The injection of the extracted
knowledge can be achieved by the addition of simple neural modules. We employ
the Convex Polytopic Model (CPM) as a feature extraction tool for DST tasks and
illustrate that the acquired features correlate with syntactic and semantic
patterns in the dialogues. This correlation facilitates a comprehensive
understanding of the linguistic features influencing the DST model's
decision-making process. We benchmark this framework on various DST tasks and
observe a notable improvement in accuracy.
