  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.
