Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

• URL: http://arxiv.org/abs/2204.06677v2
• Date: Fri, 15 Apr 2022 14:51:09 GMT
• Title: Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
• Authors: Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, Emine Yilmaz
• Abstract summary: Dialogue State Tracking (DST) aims to keep track of users' intentions during the course of a conversation. textbfDSGFNet 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.
• Score: 26.06036827056842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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