Knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus  

作　　者：Feng Gao Jin Hou Jinguang Gu Lihua Zhang 

机构地区：[1]School of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,Hubei [2]The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content,Insitute of Scientic and Technical Information of China,Beijing 100038,China [3]Wuhan University of Science and Technology Big Data Science and Engineering Research Institute,Wuhan 430065,Hubei [4]Eastchina Jiaotong University,Nanchang 330013,Jiangxi

出　　处：《Data Intelligence》2023年第3期685-706,共22页数据智能（英文）

基　　金：National key research and development program(2020AAA0108500);National Natural Science Foundation of China Project(No.U1836118);Key Laboratory of Rich Media Digital Publishing,Content Organization and Knowledge Service(No.:ZD2022-10/05).

摘　　要：Machine reading comprehension has been a research focus in natural language processing and intelligence engineering.However,there is a lack of models and datasets for the MRC tasks in the anti-terrorism domain.Moreover,current research lacks the ability to embed accurate background knowledge and provide precise answers.To address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic manner.Then,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the text.To eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the sentences.Experiment results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关 键 词：Machine reading comprehension Anti-terrorism domain Knowledge embedding Knowledge attention Mutual attention 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] C31[自动化与计算机技术—计算机科学与技术]