基于稠密连接记忆神经网络的文本推理

由于传统的端到端记忆神经网络模型特征表示能力不足、无法很好地表示各个记忆之间的联系，导致其在数据集bAbI中的位置推理和路径查找问题正确率不高，针对此问题，提出了一种结合稠密连接和多层感知机的记忆神经网络。该模型利用稠密连接与全连接层获取关系特征，增强了模型的特征表示能力。利用bAbI数据集对模型进行推理正确率的评估，实验结果表明，与传统的记忆神经网络以及端到端记忆神经网络相比，该模型能有效提升文本推理的正确率。

Text reasoning based on densely connective memory networks

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 the model can effectively improve the reasoning accuracy compare with traditional me-mory network and the existing end-to-end memory network.