一种引入核心实体关注度评估的KBQA算法  

KBQA Algorithm Introducing Core Entity Attention Evaluation

作　　者：赵卫东[1,2] 晋艳峰 张睿 林沿铮[1,2] ZHAO Weidong;JIN Yanfeng;ZHANG Rui;LIN Yanzheng(School of Software,Fudan University,Shanghai 200433,China;Shanghai Key Laboratory of Data Science,Fudan University,Shanghai 200433,China)

机构地区：[1]复旦大学软件学院,上海200433 [2]上海市数据科学重点实验室,上海200433

出　　处：《计算机科学》2024年第11期239-247,共9页Computer Science

基　　金：国家自然科学基金(71971066)。

摘　　要：目前针对复杂语义和复杂句法的知识库问答(Knowledge Base Question Answering,KBQA)研究层出不穷,但它们多以已知问题的主题实体为前提,对问题中多意图和多实体重视不足,而问句中对核心实体的识别是理解自然语言的关键。针对此问题,提出了一种引入核心实体关注度的KBQA模型。该模型基于注意力机制及注意力增强技术,对识别到的实体引用(Mention)进行重要性评估,得到实体引用关注度,去除潜在干扰项,捕获用户提问的核心实体,解决了多实体、多意图问句的语义理解问题。此外,还将评估的结果作为重要权重引入后续的问答推理中。在英文MetaQA数据集、多实体问句MetaQA数据集、多实体问句HotpotQA数据集上,与KVMem,GraftNet,PullNet等模型进行了对比实验。结果表明,针对多实体问句,所提模型在Hits@n、准确率、召回率等评估指标上均取得了更好的实验效果。There are numerous knowledge base question answering(KBQA)researches on complex semantics and complex syntax,but most of them are based on the premise that the subject entity of the question has been obtained,and insufficient attention has been paid to the multi-intentions and multi-entities in the question,and the identification of the core entity in the interrogative sentence is the key to natural language understanding.To address this problem,a KBQA model introducing core entity attention is proposed.Based on the attention mechanism and attention enhancement techniques,the proposed model assesses the importance of the recognized entity mention,obtains the entity mention attention,removes the potential interfering items,captures the core entity of the user's question,so as to solve the semantic understanding problem of multi-entity and multi-intention interrogative sentences.Evaluated results are introduced into the subsequent Q&A reasoning as importance weights.Finally,comparative experiments are conducted with KVMem,GraftNet,PullNet and other models in English MetaQA dataset,multi-entity question MetaQA dataset,and multi-entity question HotpotQA dataset.For multi-entity question,the proposed model achieves better experimental results on Hits@n,accuracy,recall and other evaluation indexes.

关 键 词：知识库问答 意图识别 实体关注度 多实体 多意图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]