Graph-based Dynamic Domain Selection for Dialogue State Tracking

Shuangyong Song, Jinwei Zhang, Hao Huang, Hongyan Xie, Haoxiang Su, Mengxiang Li, Zhongjiang He, Yongxiang Li, Ruiyu Fang

Abstract: The Dialogue State Tracking (DST) module tracks the user’s intent by populating multiple predefined slots related to the dialogue task. In recent years, various graph neural network-based DST methods have been proposed to establish graph structures capturing the correlations between domains and slots, thereby enhancing model performance. However, these methods may involve redundant connections in the graph structure. To better construct relationships between domains and slots, we introduce a graph neural network-based dialogue state tracking method called Dynamic Domain Selection Graph DST (DDSG-DST). Specifically, (1) we employ Graphormer to establish hierarchical relationships between domains and slots; (2) we propose an additional domain prediction auxiliary task to predict the domain relevant to the dialogue context; (3) based on the predicted relevant domain from the auxiliary task, we dynamically select domain node information in the graph and perform dialogue state prediction. Experimental results demonstrate that we effectively establish hierarchical relationships between domains and slots, mitigate the negative impact of redundant connections in the graph structure, and enhance model performance.