Paper: Learning The Structure Of Task-Driven Human-Human Dialogs

ACL ID P06-1026
Title Learning The Structure Of Task-Driven Human-Human Dialogs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

Data-driven techniques have been used for many computational linguistics tasks. Models derived from data are generally more robust than hand-crafted systems since they better re ect the distribution of the phenomena being modeled. With the availability of large corpora of spo- ken dialog, dialog management is now reaping the bene ts of data-driven tech- niques. In this paper, we compare two ap- proaches to modeling subtask structure in dialog: a chunk-based model of subdialog sequences, and a parse-based, or hierarchi- cal, model. We evaluate these models us- ing customer agent dialogs from a catalog service domain.