  Human conversations can evolve in many different ways, creating challenges
for automatic understanding and summarization. Goal-oriented conversations
often have meaningful sub-dialogue structure, but it can be highly
domain-dependent. This work introduces an unsupervised approach to learning
hierarchical conversation structure, including turn and sub-dialogue segment
labels, corresponding roughly to dialogue acts and sub-tasks, respectively. The
decoded structure is shown to be useful in enhancing neural models of language
for three conversation-level understanding tasks. Further, the learned
finite-state sub-dialogue network is made interpretable through automatic
summarization.
