Recent research advances in reinforcement learning in spoken dialogue systems

Matthew Frampton, Oliver Lemon

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


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. © 2009 Cambridge University Press.

Original languageEnglish
Pages (from-to)375-408
Number of pages34
JournalKnowledge Engineering Review
Issue number4
Publication statusPublished - Dec 2009


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