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        <title>Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking</title>
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        <namePart type="given">Yue</namePart>
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        <namePart type="given">Aldo</namePart>
        <namePart type="family">Lipani</namePart>
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    <name type="personal">
        <namePart type="given">Fanghua</namePart>
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        <namePart type="given">Qiang</namePart>
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            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <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 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.</abstract>
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%0 Conference Proceedings
%T Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking
%A Feng, Yue
%A Lipani, Aldo
%A Ye, Fanghua
%A Zhang, Qiang
%A Yilmaz, Emine
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F feng-etal-2022-dynamic
%X 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.
%R 10.18653/v1/2022.acl-long.10
%U https://aclanthology.org/2022.acl-long.10
%U https://doi.org/10.18653/v1/2022.acl-long.10
%P 115-126

Markdown (Informal)

[Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking](https://aclanthology.org/2022.acl-long.10) (Feng et al., ACL 2022)

• Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking (Feng et al., ACL 2022)

ACL

• Yue Feng, Aldo Lipani, Fanghua Ye, Qiang Zhang, and Emine Yilmaz. 2022. Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 115–126, Dublin, Ireland. Association for Computational Linguistics.