基于稠密连接记忆神经网络的文本推理.

Due to the traditional end-to-end memory network model had insufficient feature representation ability, it couldn't represent the relationship between each memory well, which led to the low accuracy of location reasoning and path finding in the bAbI dataset. This paper proposed a new memory network combining density connectivity and multi-layer perceptron to solve this problem. This model used density connectivity and full connected layer to capture relationship features, which enhanced the capability of feature representation. The proposed model evaluated the accuracy of text reasoning using bAbI dataset. The experimental results show that compare with traditional me-mory network and the existing end-to-end memory network, the model can effectively improve the reasoning accuracy. [ABSTRACT FROM AUTHOR]