SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking
Hwaran Lee, Jinsik Lee, Tae-Yoon Kim · 2019
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.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.