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        <title>Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation</title>
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        <namePart type="given">Haoyang</namePart>
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        <namePart type="given">Libo</namePart>
        <namePart type="family">Qin</namePart>
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    <name type="personal">
        <namePart type="given">Ting</namePart>
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    <abstract>Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation
%A Wen, Haoyang
%A Liu, Yijia
%A Che, Wanxiang
%A Qin, Libo
%A Liu, Ting
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F wen-etal-2018-sequence
%X Classic pipeline models for task-oriented dialogue system require explicit modeling the dialogue states and hand-crafted action spaces to query a domain-specific knowledge base. Conversely, sequence-to-sequence models learn to map dialogue history to the response in current turn without explicit knowledge base querying. In this work, we propose a novel framework that leverages the advantages of classic pipeline and sequence-to-sequence models. Our framework models a dialogue state as a fixed-size distributed representation and use this representation to query a knowledge base via an attention mechanism. Experiment on Stanford Multi-turn Multi-domain Task-oriented Dialogue Dataset shows that our framework significantly outperforms other sequence-to-sequence based baseline models on both automatic and human evaluation.
%U https://aclanthology.org/C18-1320
%P 3781-3792

Markdown (Informal)

[Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation](https://aclanthology.org/C18-1320) (Wen et al., COLING 2018)

• Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation (Wen et al., COLING 2018)

ACL

• Haoyang Wen, Yijia Liu, Wanxiang Che, Libo Qin, and Ting Liu. 2018. Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3781–3792, Santa Fe, New Mexico, USA. Association for Computational Linguistics.