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        <title>Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories</title>
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    <name type="personal">
        <namePart type="given">Yuri</namePart>
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        <namePart type="family">Kanashiro Pereira</namePart>
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            <title>Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP</title>
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    <abstract>The Differentiable Neural Computer (DNC), a neural network model with an addressable external memory, can solve algorithmic and question answering tasks. There are various improved versions of DNC, such as rsDNC and DNC-DMS. However, how to integrate structured knowledge into these DNC models remains a challenging research question. We incorporate an architecture for knowledge into such DNC models, i.e. DNC, rsDNC and DNC-DMS, to improve the ability to generate correct responses using both contextual information and structured knowledge. Our improved rsDNC model improves the mean accuracy by approximately 20% to the original rsDNC on tasks requiring knowledge in the dialog bAbI tasks. In addition, our improved rsDNC and DNC-DMS models also yield better performance than their original models in the Movie Dialog dataset.</abstract>
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        <url>https://aclanthology.org/2020.knlp-1.2</url>
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        <date>2020-dec</date>
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            <start>11</start>
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%0 Conference Proceedings
%T Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories
%A Murayama, Yuri
%A Kanashiro Pereira, Lis
%A Kobayashi, Ichiro
%S Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Suzhou, China
%F murayama-etal-2020-dialogue
%X The Differentiable Neural Computer (DNC), a neural network model with an addressable external memory, can solve algorithmic and question answering tasks. There are various improved versions of DNC, such as rsDNC and DNC-DMS. However, how to integrate structured knowledge into these DNC models remains a challenging research question. We incorporate an architecture for knowledge into such DNC models, i.e. DNC, rsDNC and DNC-DMS, to improve the ability to generate correct responses using both contextual information and structured knowledge. Our improved rsDNC model improves the mean accuracy by approximately 20% to the original rsDNC on tasks requiring knowledge in the dialog bAbI tasks. In addition, our improved rsDNC and DNC-DMS models also yield better performance than their original models in the Movie Dialog dataset.
%U https://aclanthology.org/2020.knlp-1.2
%P 11-20

Markdown (Informal)

[Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories](https://aclanthology.org/2020.knlp-1.2) (Murayama et al., knlp 2020)

• Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories (Murayama et al., knlp 2020)

ACL

• Yuri Murayama, Lis Kanashiro Pereira, and Ichiro Kobayashi. 2020. Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories. In Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP, pages 11–20, Suzhou, China. Association for Computational Linguistics.