Unsupervised Learning of Hierarchical Conversation Structure

Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization. Goaloriented 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 subdialogue network is made interpretable through automatic summarization.