神经问题生成前沿综述  

Frontiers in Neural Question Generation:A Literature Review

作　　者：邱嘉作 熊德意[1] QIU Jia-zuo;XIONG De-yi(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215000,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215000

出　　处：《计算机科学》2021年第6期159-167,共9页Computer Science

基　　金：科技部重点研发计划“前沿科技创新专项”(2019QY1802)。

摘　　要：问题生成是指机器主动对一段文本进行提问,生成一个自然语言的问题。神经问题生成则是完全采用端到端的训练方式,使用神经网络完成文档和答案到问题的转换,是自然语言处理中一个新兴而又重要的研究方向。文中首先对神经问题生成进行了简单介绍,包括基本概念、主流框架和评价方法。接着介绍了该研究方向的关键问题,包括输入建模、长文本处理、多任务学习、机器学习方法的应用、其他研究问题和改进点。最后,介绍了问题生成和问答系统的关系,以及问题生成的未来研究方向。Question generation means that the machine actively asks a natural language question by given a passage.Neural question generation is trained in a completely end-to-end training mode,using neural networks to convert documents and answers to questions,which is an emerging and important research direction in natural language processing.This paper first gives a brief introduction to neural question generation,including basic concepts,mainstream frameworks,and evaluation methods.Then,it introduces the key issues of question generation,including input modeling,long document processing,multi-task learning,and the application of machine learning,other issues and improvements.Finally,it introduces the relationship between question generation and question answering,as well as future research of question generation.

关 键 词：神经问题生成 编码器-解码器模型 机器阅读理解 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]