Learning the Structure of Task-Driven Human–Human Dialogs

Authors:
S. Bangalore;G. Di Fabbrizio;A. Stent
Affiliations:
AT&T Labs.-Res., Florham Park, NJ;-;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2008

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Abstract

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.