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:
- 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)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2020.knlp-1.2.pdf
- Data
- CSQA