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.

本文提出了一种称为 slot-utterance matching belief tracker（SUMBT）的通用和可扩展的置信度跟踪方法，它通过基于上下文语义向量的注意力机制来学习域 - 插槽类型和出现在话语中的插槽值之间的关系，使用非参数化的方式预测插槽值标签。在 WOZ2.0 和 MultiWOZ 两个对话语料库上的实验中，与基于插槽的方法相比，所提出的模型表现出了性能提高，并获得了最先进的联合准确度。