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JACIII Vol.27 No.3 pp. 481-489
doi: 10.20965/jaciii.2023.p0481
(2023)

Research Paper:

Toward Question-Answering with Multi-Hop Reasoning and Calculation over Knowledge Using a Neural Network Model with External Memories

Yuri Murayama ORCID Icon and Ichiro Kobayashi ORCID Icon

Ochanomizu University
2-1-1 Otsuka, Bunkyo-ku, Tokyo 112-8610, Japan

Received:
October 25, 2022
Accepted:
February 7, 2023
Published:
May 20, 2023
Keywords:
question-answering, knowledge base, multi-hop reasoning, calculation, differentiable neural computer
Abstract

The differentiable neural computer (DNC) is a neural network model with an addressable external memory that can solve algorithmic and question-answering tasks. Improved versions of the DNC have been proposed, including the robust and scalable DNC (rsDNC) and DNC-deallocation-masking-sharpness (DNC-DMS). However, integrating structured knowledge and calculations into these DNC models remains a challenging research question. In this study, we incorporate an architecture for knowledge and calculations into the DNC, rsDNC, and DNC-DMS to improve their abilities to generate correct answers for questions with multi-hop reasoning and provide calculations over structured knowledge. Our improved rsDNC model achieves the best performance for the mean top-1 accuracy, and our improved DNC-DMS model scores the highest for the top-10 accuracy in the GEO dataset. In addition, our improved rsDNC model outperforms other models in regards to the mean top-1 accuracy and mean top-10 accuracy in the augmented GEO dataset.

Overview of our proposed model

Overview of our proposed model

Cite this article as:
Y. Murayama and I. Kobayashi, “Toward Question-Answering with Multi-Hop Reasoning and Calculation over Knowledge Using a Neural Network Model with External Memories,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 481-489, 2023.
Data files:
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