PROBABILISTIC METHODS IN SPOKEN DIALOGUE SYSTEMS
This paper presents a probabilistic framework for modelling spoken dialogue systems. On the assumption that the overall system behaviour can be represented as a Markov Decision Process, the optimisation of dialogue management strategy using reinforcement learning is reviewed. Examples of learning behaviour are presented for both dynamic programming and sampling methods, but the latter is preferred. The paper concludes by noting the importance of user simulation models for the practical application of these techniques and the need for developing methods of mapping system features in order to achieve sufficiently compact state spaces.
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