Multi-domain dialogue state tracking via dual dynamic graph with hierarchical slot selector

Abstract

Dialogue state tracking aims to maintain user intent as a consistent state across multi-domains to accomplish natural dialogue systems. However, previous researches often fall short in capturing the difference of multiple slot types and fail to adequately consider the selection of discerning information. The increase in unnecessary information correlates with a decrease in predictive performance. Therefore, the careful selection of high-quality information is imperative. Moreover, considering that the types of essential and available information vary for each slot, the process of selecting appropriate information may also differ. To address these issues, we propose HS2DG-DST, a Hierarchical Slot Selector and Dual Dynamic Graph-based DST. Our model is designed to provide maximum information for optimal value prediction by clearly exploiting the need for differentiated information for each slot. First, we hierarchically classify slot types based on the multiple properties. Then, two dynamic graphs provide highly relevant information to each slot. Experimental results on MultiWOZ datasets demonstrate that our model outperforms state-of-the-art models.