  Inducing a meaningful structural representation from one or a set of
dialogues is a crucial but challenging task in computational linguistics.
Advancement made in this area is critical for dialogue system design and
discourse analysis. It can also be extended to solve grammatical inference. In
this work, we propose to incorporate structured attention layers into a
Variational Recurrent Neural Network (VRNN) model with discrete latent states
to learn dialogue structure in an unsupervised fashion. Compared to a vanilla
VRNN, structured attention enables a model to focus on different parts of the
source sentence embeddings while enforcing a structural inductive bias.
Experiments show that on two-party dialogue datasets, VRNN with structured
attention learns semantic structures that are similar to templates used to
generate this dialogue corpus. While on multi-party dialogue datasets, our
model learns an interactive structure demonstrating its capability of
distinguishing speakers or addresses, automatically disentangling dialogues
without explicit human annotation.
