Scaling POMDPs for Spoken Dialog Management

Control in spoken dialog systems is challenging largely because automatic speech recognition is unreliable, and hence the state of the conversation can never be known with certainty. Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for planning and control in this context; however, POMDPs face severe scalability challenges, and past work has been limited to trivially small dialog tasks. This paper presents a novel POMDP optimization technique-composite summary point-based value iteration (CSPBVI)-which enables optimization to be performed on slot-filling POMDP-based dialog managers of a realistic size. Using dialog models trained on data from a tourist information domain, simulation results show that CSPBVI scales effectively, outperforms non-POMDP baselines, and is robust to estimation errors.