Parallel Computing and Practical Constraints when applying the Standard POMDP Belief Update Formalism to Spoken Dialogue Management

Abstract

We explore the commonly stated assumption that the standard POMDP formalism for belief updates cannot be directly applied to Dialogue Management for Spoken Dialogue Systems (SDSs) due to the computational intractability of maintaining a large belief state space. Focusing on SDSs, as this application has particular bounds in terms of “real-time” belief updates and potentially massive numbers of observations, we quantify computational constraints both in terms of compute time and memory. We establish a level of complexity of SDS task below which a direct implementation of the standard POMDP formalism is possible and beyond which some form of compressed representation is required. We find that computation time of POMDP belief updates is rarely an issue. Memory size and latency tend to be the dominant constraints. Low-latency, shared-memory architectures are more suitable than General Purpose Graphics Processing Units (GPGPUs) or largescale cluster/cloud infrastructure. One assumption, that users do not change their goal during a dialogue, has significant beneficial impacts on memory requirements allowing for practical POMDP SDSs which have millions of states.