POMDP-Based Statistical Spoken Dialog Systems: A Review This paper presents the theory and practice of belief tracking, policy optimization, parameter estimation, and fast learning.
Steve J. Young, Milica Gašić, Blaise Thomson, J. D. Williams · 2013
Statistical dialog systems (SDSs) are motivated by the need for a data-driven framework that reduces the cost of laboriously handcrafting complex dialog managers and that provides robustness against the errors created by speech re- cognizers operating in noisy environments. By including an explicit Bayesian model of uncertainty and by optimizing the policy via a reward-driven process, partially observable Markov decision processes (POMDPs) provide such a frame- work. However, exact model representation and optimization is computationally intractable. Hence, the practical application of POMDP-based systems requires efficient algorithms and carefully constructed approximations. This review article pro- vides an overview of the current state of the art in the devel- opment of POMDP-based spoken dialog systems.