文献类型：会议论文

英文题名：Slot Attention with Value Normalization for Multi-Domain Dialogue State Tracking

作者：Wang, Yexiang[1];Guo, Yi[1];Zhu, Siqi[1]

机构：[1]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：Conference on Empirical Methods in Natural Language Processing (EMNLP)

会议日期：NOV 16-20, 2020

会议地点：ELECTR NETWORK

语种：英文

摘要：Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence. In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span. VN is designed specifically for the use of ontology, which can convert supporting spans to the values. Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52% on MultiWOZ 2.0 and 54.86% on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. The results show that even if only 30% ontology is used, VN can also contribute to our model.