融合多头注意力机制和孪生网络的语义匹配方法  

Semantic Matching Method Integrating Multi-head Attention Mechanism and Siamese Network

作　　者：臧洁[1] 周万林 王妍[1] ZANG Jie;ZHOU Wanlin;WANG Yan(College of Information,Liaoning University,Shenyang 110036,China)

机构地区：[1]辽宁大学信息学院,沈阳110036

出　　处：《计算机科学》2023年第12期294-301,共8页Computer Science

基　　金：国家重点研发计划(2019YFB1405804)。

摘　　要：考虑企业资源与客户需求匹配问题,现有的方法存在资源和需求封装不够准确以及匹配效果无法满足用户需求等问题。为解决企业资源与需求描述的多样性和歧义性,提出了动态自定义模板封装。针对封装后的需求与资源大多都是中文短文本这一特点,兼顾句子间语义的差异性和相似性,提出了融合多头注意力机制和孪生网络的交互型文本匹配模型。模型使用字词混向量作为输入增强文本的语义信息,将孪生网络与多头注意力机制相融合,作为独立单元提取上下文的语义特征并使语义特征充分交互。为了验证模型的有效性,在经典数据集LCQMC和自我构建的CSMD数据集上对模型进行了实验,结果表明所提模型在准确率和性能等方面均有不同程度的提升,为企业资源与需求提供了更精准的匹配方法。Considering the matching problem of enterprise resources and customer requirements,the existing methods have the problems that the resource and requirement encapsulation is not accurate enough and the matching effect can't satisfy uses'requirement.In order to solve the problem of diversity and ambiguity of enterprise resource and requirement description,this paper proposes the dynamic user-defined template encapsulation.According to the feature that most of the encapsulated requirements and resources are Chinese short texts,an interactive text matching model which integrates multi-head attention mechanism and sia-mese network is proposed.The semantic differences and similarities between sentences are considered in this model.It uses word mixing vectors as input to enhance the semantic information of the text,combines the Siamese network with the multi-head attention mechanism,and extractes the semantic features of the context as an independent unit to fully interact with the semantic features.In order to verify the effectiveness of the model,the classical data set LCQMC and the self-constructed CSMD data set are used to conduct experiments on the model.The results show that the accuracy and performance of the model are improved in different degrees,which provides a more accurate matching method for enterprise resources and requirements.

关 键 词：自定义模板 语义匹配 孪生网络 多头注意力机制 双向GRU 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]