Abstract:

We design a log-linear probabilistic model for solving the dialog management task. In both planning and learning we optimize the same objective function: the expected reward. Rather than performing full policy optimization, we perform on-line estimation of the optimal action as a belief-propagation inference step. We employ context-free grammars to describe our variable spaces, which enables us to define rich features. To scale our approach to large variable spaces, we use particle belief propagation. Experiments show that the model is able to choose system actions that yield a high expected reward, outperforming its POMDP-like log-linear counterpart and a hand-crafted rule-based system.