Combining POMDPs trained with user simulations and rule-based dialogue management in a spoken dialogue system
Sebastian Varges, Silvia Quarteroni, Giuseppe Riccardi, Alexei V. Ivanov, Pierluigi Roberti · 2009
Over several years, we have developed an approach to spoken dialogue systems that includes rule-based and trainable dialogue managers, spoken language understanding and generation modules, and a comprehensive dialogue system architecture.We present a Reinforcement Learning-based dialogue system that goes beyond standard rule-based models and computes on-line decisions of the best dialogue moves.The key concept of this work is that we bridge the gap between manually written dialog models (e.g.rule-based) and adaptive computational models such as Partially Observable Markov Decision Processes (POMDP) based dialogue managers.