After witnessing the soaring development of the Internet in the past decades,the problem of Cyber Content Security,which is considered as one of the core tasks of network governance,has become increasingly prominent.Text content is the most pivotal research object of cyber content security.However,the inherent ambiguous and flexibility of natural language bring great difficulties to public opinion monitoring and cyber content governance on the Internet.Therefore,how to accurately understand the text content is the key issue of cyber content governance.At present,the core supporting technology of text content understanding is based on Natural Language Processing.As a comprehensive task in the field of Natural Language Processing,Machine Reading Comprehension can analyze the network content in depth and achieve a comprehensive understanding,which plays an important role in the monitoring of network public opinion and the governance of cyber content.In recent years,Deep Learning technology has made remarkable achievements in many fields,such as Pattern Recognition,text classification and Natural Language Processing.Likewise,Machine Reading Comprehension methods based on Deep Learning have been widely studied.Especially in recent years,the publication of various large-scale datasets has accelerated the development of neural Machine Reading Comprehension,and various ma-chine reading models combining different neural networks have been proposed successively.The purpose of this paper is to review various neural machine reading models.Firstly,the development history and research status of Machine Reading Comprehension are introduced.Then,the task definition of Machine Reading Comprehension is expounded,and represen-tative datasets and neural machine reading models are presented.The latest research progress of four new trends is intro-duced.Finally,the existing problems of the neural machine reading model are put forward,how Machine Reading Com-prehension methods are applied to solve the problem of Cyber content governance is analyzed,and the future development trend is forecasted.
维普
万方数据