SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking

In goal-oriented dialog systems, belief trackers estimate the probability distribution of slotvalues at every dialog turn. Previous neural approaches have modeled domain-and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slotvalues appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way.