Learning the structure of task-driven human-human dialogs

  • Authors:
  • Srinivas Bangalore;Giuseppe Di Fabbrizio;Amanda Stent

  • Affiliations:
  • AT&T Labs-Research, Florham Park, NJ;AT&T Labs-Research, Florham Park, NJ;Stony Brook University, Stony Brook, NY

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

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Abstract

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 reflect the distribution of the phenomena being modeled. With the availability of large corpora of spoken dialog, dialog management is now reaping the benefits of data-driven techniques. In this paper, we compare two approaches to modeling subtask structure in dialog: a chunk-based model of subdialog sequences, and a parse-based, or hierarchical, model. We evaluate these models using customer agent dialogs from a catalog service domain.