@inproceedings{murayama-etal-2020-dialogue,
title = "Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories",
author = "Murayama, Yuri and
Kanashiro Pereira, Lis and
Kobayashi, Ichiro",
editor = "Shalom, Oren Sar and
Panchenko, Alexander and
dos Santos, Cicero and
Logacheva, Varvara and
Moschitti, Alessandro and
Dagan, Ido",
booktitle = "Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.knlp-1.2",
pages = "11--20",
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.",
}
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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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%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
%Y Shalom, Oren Sar
%Y Panchenko, Alexander
%Y dos Santos, Cicero
%Y Logacheva, Varvara
%Y Moschitti, Alessandro
%Y Dagan, Ido
%S Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
%D 2020
%8 December
%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)
ACL