Using particle filters to track dialogue state

The bene t of tracking a probability distribution over multiple dialogue states has been demonstrated in the literature. However, the dialogue state in past work has been limited to a small number of variables, and growing the number of variables in the dialogue state prevents the probability distribution from being updated in real-time. This paper shows how the number of variables composing the dialogue state can be increased while maintaining response times suitable for a spoken dialogue system. Rather than performing exact inference using the joint distribution over all variables, a particle Iter is employed to compute an approximate update. Dialogue states (particles) are sampled, weighted by their agreement with the speech recognition results, and marginalized to produce a new distribution over each variable. Results on a spoken dialogue system for troubleshooting show that a relatively small number of particles are required to achieve performance close to an exact update, enabling the dialogue system to run in realtime.