Review on Interactive Question Answering Techniques Based on Deep Learning

Abstract

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.

Keywords

• dialogue system|interactive question answering|background|pre-trained models