Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yılmaz · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation.In DST, modelling the relations among domains and slots is still an under-studied problem.Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains.To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations.It also uses the schemata to facilitate knowledge transfer to new domains.DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder.Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1,and MultiWOZ2.2),show that DSGFNet outperforms existing methods.