SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
This addresses the lack of flexibility in domain ontology configurations for goal-oriented dialog systems, though it is an incremental improvement over existing neural methods.
The paper tackled the problem of inflexible belief tracking in dialog systems by proposing SUMBT, a universal and scalable belief tracker that uses slot-utterance matching and non-parametric prediction, achieving state-of-the-art joint accuracy on WOZ 2.0 and MultiWOZ datasets.
In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values 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 slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.