基于深度学习的交互式问答研究综述

Review on Interactive Question Answering Techniques Based on Deep Learning

相比传统的一问一答,交互式问答增加了对话上下文和背景等信息,这为理解用户输入和推理答案带来了新的挑战。首先,用户输入不再局限于问题,还可以是告知问题细节、反馈答案可行与否等带有意图的语句,因此需要理解对话中每个语句的意图。其次,交互式问答允许多个角色同时参与一个问题的讨论,为每个角色生成个性化的答案,因此需要理解对话中存在的角色。再次,当交互式问答围绕一段背景文本展开时,需要理解这段背景文本,并从中抽取出问题的答案。文章对交互式问答的发展及前沿动态进行了调研,分别对无背景交互式问答、有背景交互式问答以及迁移学习在交互式问答的应用3个子方向的新方法和新发现进行了介绍,并对交互式问答的研究前景进行了分析和展望。

Compared to the traditional question answering(QA),interactive question answering(IQA)considers dialogue context and background information,which brings new challenges to understand user input and reason answers.First of all,user input is not only limited to questions,but can also be utterances that inform the details of the question and give feedback on whether the answer is feasible or not.Therefore,it is necessary to understand the intent of each utterance in the dialogue.Secondly,IQA allows multiple characters to discuss a question at the same time,generating personalized answers.So,it is necessary to understand different characters and identify them from each other.Thirdly,when IQA revolves around a background document,it is necessary to understand this document and extract answers from it.This paper reviews recent development in three subareas:IQA without background,IQA with background,and the application of transfer learning in IQA,and finally discusses the future perspective of interactive question answering.