Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories

Yuri Murayama, Lis Kanashiro Pereira, Ichiro Kobayashi


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.
Anthology ID:
2020.knlp-1.2
Volume:
Proceedings of Knowledgeable NLP: the First Workshop on Integrating Structured Knowledge and Neural Networks for NLP
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
knlp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–20
Language:
URL:
https://aclanthology.org/2020.knlp-1.2
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Dialogue over Context and Structured Knowledge using a Neural Network Model with External Memories (Murayama et al., knlp 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/paclic-22-ingestion/2020.knlp-1.2.pdf
Data
CSQA