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        <title>Parallel Interactive Networks for Multi-Domain Dialogue State Generation</title>
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    <name type="personal">
        <namePart type="given">Junfan</namePart>
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        <namePart type="given">Richong</namePart>
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    <name type="personal">
        <namePart type="given">Yongyi</namePart>
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    <name type="personal">
        <namePart type="given">Jie</namePart>
        <namePart type="family">Xu</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yang</namePart>
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    <abstract>The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.151</identifier>
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        <date>2020-11</date>
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            <start>1921</start>
            <end>1931</end>
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%0 Conference Proceedings
%T Parallel Interactive Networks for Multi-Domain Dialogue State Generation
%A Chen, Junfan
%A Zhang, Richong
%A Mao, Yongyi
%A Xu, Jie
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-parallel
%X The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.
%R 10.18653/v1/2020.emnlp-main.151
%U https://aclanthology.org/2020.emnlp-main.151
%U https://doi.org/10.18653/v1/2020.emnlp-main.151
%P 1921-1931

Markdown (Informal)

[Parallel Interactive Networks for Multi-Domain Dialogue State Generation](https://aclanthology.org/2020.emnlp-main.151) (Chen et al., EMNLP 2020)

• Parallel Interactive Networks for Multi-Domain Dialogue State Generation (Chen et al., EMNLP 2020)

ACL

• Junfan Chen, Richong Zhang, Yongyi Mao, and Jie Xu. 2020. Parallel Interactive Networks for Multi-Domain Dialogue State Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1921–1931, Online. Association for Computational Linguistics.