User Goal Change Model for Spoken Dialog State Tracking

In this paper, a Maximum Entropy Markov Model (MEMM) for dialog state tracking is proposed to efficiently handle user goal evolvement in two steps. The system first predicts the occurrence of a user goal change based on linguistic features and dialog context for each dialog turn, and then the proposed model could utilize this user goal change information to infer the most probable dialog state sequence which underlies the evolvement of user goal during the dialog. It is believed that with the suggested various domain independent feature functions, the proposed model could better exploit not only the intra-dependencies within long ASR N-best lists but also the inter-dependencies of the observations across dialog turns, which leads to more efficient and accurate dialog state inference.