Constructing Smart Consulting Q&A System Based on Machine Reading Comprehension
[Objective]This paper aims to improve the smart consulting systems to effectively answer academic questions.[Methods]We utilized deep learning,machine reading comprehension,data augmentation,information retrieval,and semantic similarity techniques to construct datasets and an academic knowledge question-answering system.Additionally,we designed a multi-paragraph recall metric to address the characteristics of academic literature and enhance retrieval accuracy with multidimensional features.[Results]Our new model's ROUGE-L score reached 0.7338,with a question-answering accuracy of 88.65％and a multi-paragraph recall metric accuracy of 88.38％.[Limitations]We only examined the new model with single-domain content,which may limit the system's performance in dealing with complex issues involving multiple domains.[Conclusions]The deep integration of machine reading comprehension technology with reference services can enhance the efficiency and sharing of academic resources and provide more comprehensive and accurate information support for researchers.
Deep LearningMachine Reading ComprehensionSmart Consulting ServicesQ&A Systems
NETL
NSTL
万方数据
