  Recently several deep learning based models have been proposed for end-to-end
learning of dialogs. While these models can be trained from data without the
need for any additional annotations, it is hard to interpret them. On the other
hand, there exist traditional state based dialog systems, where the states of
the dialog are discrete and hence easy to interpret. However these states need
to be handcrafted and annotated in the data. To achieve the best of both
worlds, we propose Latent State Tracking Network (LSTN) using which we learn an
interpretable model in unsupervised manner. The model defines a discrete latent
variable at each turn of the conversation which can take a finite set of
values. Since these discrete variables are not present in the training data, we
use EM algorithm to train our model in unsupervised manner. In the experiments,
we show that LSTN can help achieve interpretability in dialog models without
much decrease in performance compared to end-to-end approaches.
