  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
\textbf{D}ynamic \textbf{S}chema \textbf{G}raph \textbf{F}usion
\textbf{Net}work (\textbf{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.
