From local verification to global reasoning: Exploiting slot-accompanying update for improved slot selection

Abstract

The goal of dialogue-state tracking (DST) is to determine the current state of a dialogue by analysing the entire preceding dialogue context. Nonetheless, current approaches frequently fail to account for the significance of concurrent updates, where related slots must be updated simultaneously based on their historical relationships, even in the absence of explicit signals in the current dialogue turn. To address this limitation, we introduce From Local Verification to Global Reasoning (FLV2GR), an innovative method that improves slot-update selection by combining local verification of present dialogue details with global reasoning over historical dialogue data. Our approach utilizes a graph neural network (GNN) to model and infer interdependencies between slots, enabling the identification of accompanying update relationships that are frequently overlooked by other approaches. This comprehensive selection mechanism improves the precision of slot updates, thereby enhancing overall DST performance. The FLV2GR model establishes a new performance benchmark on the MultiWOZ 2.1, 2.2, and 2.4 datasets, showcasing its effectiveness in capturing both local and global dialogue dynamics for more precise and reliable DST.¹