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        <title>A Working Memory Model for Task-oriented Dialog Response Generation</title>
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    <name type="personal">
        <namePart type="given">Xiuyi</namePart>
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        <namePart type="given">Jiaming</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
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    <abstract>Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the “activated” contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1258</identifier>
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%0 Conference Proceedings
%T A Working Memory Model for Task-oriented Dialog Response Generation
%A Chen, Xiuyi
%A Xu, Jiaming
%A Xu, Bo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-working
%X Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the “activated” contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.
%R 10.18653/v1/P19-1258
%U https://aclanthology.org/P19-1258
%U https://doi.org/10.18653/v1/P19-1258
%P 2687-2693

Markdown (Informal)

[A Working Memory Model for Task-oriented Dialog Response Generation](https://aclanthology.org/P19-1258) (Chen et al., ACL 2019)

• A Working Memory Model for Task-oriented Dialog Response Generation (Chen et al., ACL 2019)

ACL

• Xiuyi Chen, Jiaming Xu, and Bo Xu. 2019. A Working Memory Model for Task-oriented Dialog Response Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2687–2693, Florence, Italy. Association for Computational Linguistics.