This paper proposes an unsupervised approach to user simulation in order to automatically furnish updates and assessments of a deployed spoken dialog system. The proposed method adopts a dynamic Bayesian network to infer the unobservable true user action from which the parameters of other components are naturally derived. To verify the quality of the simulation, the proposed method was applied to the Let's Go domain (Raux et al., 2005) and a set of measures was used to analyze the simulated data at several levels. The results showed a very close correspondence between the real and simulated data, implying that it is possible to create a realistic user simulator that does not necessitate human intervention.
['Sungjin Lee', 'Maxine Eskenazi']
An Unsupervised Approach to User Simulation: Toward Self-Improving Dialog Systems