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        <title>Global-Locally Self-Attentive Encoder for Dialogue State Tracking</title>
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            <namePart type="given">Yusuke</namePart>
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    <abstract>Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3% joint goal accuracy and 96.4% request accuracy on the WoZ state tracking task, outperforming prior work by 3.9% and 4.8%. On the DSTC2 task, our model obtains 74.7% joint goal accuracy and 97.3% request accuracy, outperforming prior work by 1.3% and 0.8%</abstract>
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    <identifier type="doi">10.18653/v1/P18-1135</identifier>
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%0 Conference Proceedings
%T Global-Locally Self-Attentive Encoder for Dialogue State Tracking
%A Zhong, Victor
%A Xiong, Caiming
%A Socher, Richard
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhong-etal-2018-global
%X Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of task-oriented dialogue systems. In this paper, we propose the Global-Locally Self-Attentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to shares parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states. GLAD obtains 88.3% joint goal accuracy and 96.4% request accuracy on the WoZ state tracking task, outperforming prior work by 3.9% and 4.8%. On the DSTC2 task, our model obtains 74.7% joint goal accuracy and 97.3% request accuracy, outperforming prior work by 1.3% and 0.8%
%R 10.18653/v1/P18-1135
%U https://aclanthology.org/P18-1135
%U https://doi.org/10.18653/v1/P18-1135
%P 1458-1467

Markdown (Informal)

[Global-Locally Self-Attentive Encoder for Dialogue State Tracking](https://aclanthology.org/P18-1135) (Zhong et al., ACL 2018)

• Global-Locally Self-Attentive Encoder for Dialogue State Tracking (Zhong et al., ACL 2018)

ACL

• Victor Zhong, Caiming Xiong, and Richard Socher. 2018. Global-Locally Self-Attentive Encoder for Dialogue State Tracking. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1458–1467, Melbourne, Australia. Association for Computational Linguistics.