结合多粒度信息的文本匹配融合模型

Text Matching Fusion Model Combining Multi-granularity Information

常规的文本匹配模型大致分为基于表示的文本匹配模型和基于交互的文本匹配模型。由于基于表示的文本匹配模型容易失去语义焦点,而基于交互的文本匹配模型会忽视全局信息,文中提出了结合多粒度信息的文本匹配融合模型。该模型通过交互注意力和表示注意力将两种文本匹配模型进行了融合,然后利用卷积神经网络提取了文本中存在的多个不同级别的粒度信息,使得模型既能抓住局部的重要信息又能获取全局的语义信息。在3组不同的文本匹配任务上的实验结果表明,所提出的模型在NDCG@5评价指标上分别优于其他最优模型5.3%,0.4%,1.5%。通过提取文本中的多个粒度信息并结合交互注意力和表示注意力,提出的模型能够有效地关注不同级别的文本信息,解决了传统模型在文本匹配过程中易失去语义焦点和忽视全局信息的问题。

Conventional text matching methods are basically divided into representational text matching models and interaction-based text matching models.Since the representation-based text matching model is easy to lose semantic focus and the interaction-based text matching model ignores global information,a text matching fusion model combining multi-granularity information is proposed in this paper.This model fuses two text matching models through interactive attention and expressing attention,and then uses convolutional neural networks to extract multiple different levels of granularity information presented in the text.Then the local important information and global semantic information can be captured.The experimental results on three different text matching tasks show that the proposed model outperform other optimal models by 5.3%,0.4%,1.5%on the NDCG@5 evaluation index respectively.By extracting multiple granularity information of the text and combining interactive attention and expressed attention,the proposed model can effectively pay attention to the text information of different levels,and solve the problem of losing semantics and ignoring global information during the text matching process in the traditional models.