Toward Habitable Assistance from Spoken Dialogue Systems

Authors

  • Susan L. Epstein Hunter College
  • Rebecca J. Passonneau Columbia University
  • Tiziana Ligorio Hunter College
  • Joshua Gordon Columbia University

DOI:

https://doi.org/10.1609/aaai.v26i2.18970

Abstract

Spoken dialogue is increasingly central to systems that assist people. As the tasks that people and machines speak about together become more complex, however, users’ dissatisfaction with those systems is an important concern. This paper presents a novel approach to learning for spoken dialogue systems. It describes embedded wizardry, a methodology for learning from skilled people, and applies it to a library whose patrons order books by telephone. To address the challenges inherent in this application, we introduce RFW+, a domain-independent, feature-selection method that considers feature categories. Models learned with RFW+ on embedded-wizard data improve the performance of a traditional spoken dialogue system.

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Published

2012-07-22

How to Cite

Epstein, S., Passonneau, R., Ligorio, T., & Gordon, J. (2012). Toward Habitable Assistance from Spoken Dialogue Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 26(2), 2281-2286. https://doi.org/10.1609/aaai.v26i2.18970