  We present a novel architecture for explainable modeling of task-oriented
dialogues with discrete latent variables to represent dialogue actions. Our
model is based on variational recurrent neural networks (VRNN) and requires no
explicit annotation of semantic information. Unlike previous works, our
approach models the system and user turns separately and performs database
query modeling, which makes the model applicable to task-oriented dialogues
while producing easily interpretable action latent variables. We show that our
model outperforms previous approaches with less supervision in terms of
perplexity and BLEU on three datasets, and we propose a way to measure dialogue
success without the need for expert annotation. Finally, we propose a novel way
to explain semantics of the latent variables with respect to system actions.
