  Can we discover dialog structure by dividing utterances into labelled
clusters. Can these labels be generated from the data. Typically for dialogs we
need an ontology and use that to discover structure, however by using
unsupervised classification and self-labelling we are able to intuit this
structure without any labels or ontology. In this paper we apply SCAN (Semantic
Clustering using Nearest Neighbors) to dialog data. We used BERT for pretext
task and an adaptation of SCAN for clustering and self labeling. These clusters
are used to identify transition probabilities and create the dialog structure.
The self-labelling method used for SCAN makes these structures interpretable as
every cluster has a label. As the approach is unsupervised, evaluation metrics
is a challenge, we use statistical measures as proxies for structure quality
