摘要： 现有的文本语义匹配方法大多基于简单的注意力机制进行交互，较少考虑文本自身结构信息和文本之间原始信息的的交互。针对2个中文文本的语义匹配问题，构建一个多角度信息交互的文本匹配模型MAII。分别从颗粒、局部、全局3个角度计算2个文本深层次的语义交互矩阵，同时考虑语序信息之间和结构信息之间的交互以及文本内部的依赖关系，从而得到含有丰富信息的语义向量，并通过语义推理计算出两文本之间的语义匹配度。实验结果表明，相比在英文数据集上表现良好的DSSM、ESIM和DIIN模型，MAII模型在CCKS 2018问句匹配大赛的中文数据集上达到77.77%的准确率，表现出更好的匹配性能。

关键词: 信息交互, 语义匹配, 注意力机制, 深度神经网络, 中文数据集

Abstract: The existing semantic text matching methods are mostly based on a simple attention mechanism for interactions, and less consideration is given to the structural information of the text itself and the original information interactions between the texts.To address the semantic matching between two Chinese texts, a text matching model, MAII, is proposed based on multi-angle information interactions.The model calculates the deep-level semantic interaction matrix of the two texts at the granularity level, local level and global level respectively.At the same time, the interactions between the word order information, the interactions between structural information, and the internal dependencies of the two texts are also considered.On this basis, a semantic vector with rich information is obtained, and then the semantic matching degree between the two texts is calculated through semantic reasoning.Experimental results show that compared to DSSM, ESIM and DIIN models that perform well on the English data sets, the MAII model exhibits better performance on the Chinese data set of CCKS 2018 Question Matching Competition with the accuracy reaching 77.77%.

Key words: information interaction, semantic matching, attention mechanism, Deep Neural Network(DNN), Chinese dataset

中图分类号: 

• TP391