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

Abstract

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.

6 pages, 2 figures, The 57th Annual Meeting of the Association for Computational Linguistics (ACL)

Cited by in corpus (5)

• From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
• Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
• Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
• Efficient Dialogue State Tracking by Masked Hierarchical Transformer
• NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation