Skip to main content
×
Home
    • Aa
    • Aa

Recent research advances in Reinforcement Learning in Spoken Dialogue Systems

  • Matthew Frampton (a1) and Oliver Lemon (a2)
Abstract
Abstract

This paper will summarize and analyze the work of the different research groups who have recently made significant contributions in using Reinforcement Learning techniques to learn dialogue strategies for Spoken Dialogue Systems (SDSs). This use of stochastic planning and learning has become an important research area in the past 10 years, since it promises automatic data-driven optimization of the behavior of SDSs that were previously hand-coded by expert developers. We survey the most important developments in the field, compare and contrast the different approaches, and describe current open problems.

Copyright
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

A. Cheyer , D. Martin 2001. The open agent architecture. Journal of Autonomous Agents and Multi-Agent Systems 4(1/2), 143148.

M. English , P. Heeman 2005. Learning mixed-initiative dialog strategies by using reinforcement learning on both conversants. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, 10111018.

N. Fraser , G. Gilbert 1991. Simulating speech systems. Computer Speech and Language 5(1), 8199.

J. Henderson , O. Lemon , K. Georgila 2008. Hybrid reinforcement/supervised learning of dialogue policies from fixed datasets. Computational Linguistics 34(4), 487511.

O. Lemon , K. Georgila , J. Henderson 2006a. Evaluating effectiveness and portability of reinforcement learned strategies. In Proceedings of the IEEE/ACL Workshop on Spoken Language Technology, Palm Beach, Aruba, 178181.

E. Levin , R. Pieraccini , W. Eckert 2000. A stochastic model of computer-human interaction for learning dialogue strategies. IEEE Transactions On Speech and Audio Processing 8(1), 1123.

K. Scheffler , S. Young 2002. Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning. In Proceedings of the Human Language Technology conference, Marcus, M. (ed.). San Diego, USA, 1219.

J. Tetreault , D. Litman 2006. Comparing the utility of state features in spoken dialogue using reinforcement learning. In Proceedings of the Human Language Technology Conference/North American chapter of the Association for Computational Linguistics annual meeting, Moore, R.C., Bilmes, J.A. & Chu-Carroll, J. (eds). New York, USA, 272279.

M. Walker , J. Fromer , S. Narayanan 1998. Learning optimal dialogue strategies: a case study of a spoken dialogue agent for email. In Proceedings of the 36th Annual Meeting of the Association of Computational Linguistics, 13451352.

M. Walker , C. Kamm , D. Litman 2000. Towards developing general models of usability with PARADISE. Natural Language Engineering 6(3), 363377.

M. Walker , D. Litman , C. Kamm , A. Abella 1997. PARADISE: a framework for evaluating spoken dialogue agents. In Proceedings of the 35th Annual Meeting of the Association of Computational Linguistics, 271280.

J. Williams , S Young . 2007. Partially Observable Markov Decision Processes for spoken dialog systems. Computer Speech and Language 21(2), 231422.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

The Knowledge Engineering Review
  • ISSN: 0269-8889
  • EISSN: 1469-8005
  • URL: /core/journals/knowledge-engineering-review
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×