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        <title>Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking</title>
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        <namePart type="given">Su</namePart>
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    <name type="personal">
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    <abstract>Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.68</identifier>
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%0 Conference Proceedings
%T Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
%A Zhu, Su
%A Li, Jieyu
%A Chen, Lu
%A Yu, Kai
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhu-etal-2020-efficient
%X Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can outperform strong baselines, and the previous state-of-the-art method (SOM-DST) can also be improved by our proposed schema graph.
%R 10.18653/v1/2020.findings-emnlp.68
%U https://aclanthology.org/2020.findings-emnlp.68
%U https://doi.org/10.18653/v1/2020.findings-emnlp.68
%P 766-781

Markdown (Informal)

[Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking](https://aclanthology.org/2020.findings-emnlp.68) (Zhu et al., Findings 2020)

• Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking (Zhu et al., Findings 2020)

ACL

• Su Zhu, Jieyu Li, Lu Chen, and Kai Yu. 2020. Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 766–781, Online. Association for Computational Linguistics.