Learning the Structure of Task-Driven Human–Human Dialogs

With the availability of large corpora of spoken dialog, it is now possible to use data-driven techniques to build and use models of task-oriented dialogs. In this paper, we use data-driven techniques to build task structures for individual dialogs, and use the dialog task structures for: dialog act classification, task/subtask classification, task/subtask prediction, and dialog act prediction. We evaluate our approach using a corpus of customer/agent dialogs from a catalog service domain. This paper demonstrates the feasibility of using corpora of human–human conversation to learn dialog models suitable for human–computer dialog applications.