Discovering latent structure in task-oriented dialogues

A key challenge for computational conver-sation models is to discover latent struc-ture in task-oriented dialogue, since it pro-vides a basis for analysing, evaluating, and building conversational systems. We pro-pose three new unsupervised models to discover latent structures in task-oriented dialogues. Our methods synthesize hidden Markov models (for underlying state) and topic models (to connect words to states). We apply them to two real, non-trivial datasets: human-computer spoken dia-logues in bus query service, and human-human text-based chats from a live tech-nical support service. We show that our models extract meaningful state represen-tations and dialogue structures consistent with human annotations. Quantitatively, we show our models achieve superior per-formance on held-out log likelihood eval-uation and an ordering task.