Question answering systems are recognized as popular and frequently effective means of information seeking on the web. In such systems, information seekers can receive a concise response to their query by presenting their questions in natural language. Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems. On the one hand, the user can ask questions in normal language and locate the actual response to her inquiry; on the other hand, the system can prolong the question-answering session into a dialogue if there are multiple probable replies, very few, or ambiguities in the initial request. By permitting the user to ask more questions, interactive question answering enables users to dynamically interact with the system and receive more precise results. This survey offers a detailed overview of the interactive question-answering methods that are prevalent in current literature. It begins by explaining the foundational principles of question-answering systems, hence defining new notations and taxonomies to combine all identified works inside a unified framework. The reviewed published work on interactive question-answering systems is then presented and examined in terms of its proposed methodology, evaluation approaches, and dataset/application domain. We also describe trends surrounding specific tasks and issues raised by the community, so shedding light on the future interests of scholars. Our work is further supported by a GitHub page with a synthesis of all the major topics covered in this literature study. https://sisinflab.github.io/interactive-question-answering-systems-survey/

翻译：问答系统被公认为网络上流行且有效的信息检索手段。在此类系统中，信息寻求者可以通过自然语言提出问题，从而获得简洁的答案。交互式问答是近年来提出并日益流行的解决方案，它处于问答系统与对话系统的交叉点。一方面，用户可以使用日常语言提问并定位到实际查询结果；另一方面，当初始请求存在多个可能答案、答案极少或存在歧义时，系统可将问答过程延伸为对话。通过允许用户提出更多问题，交互式问答使用户能够与系统动态交互并获得更精确的结果。本综述详细概述了当前文献中主流的交互式问答方法。首先阐述了问答系统的基本原理，由此定义新的符号体系和分类法，将所有识别的相关工作整合到统一框架中。随后以方法学、评估方式和数据集/应用领域为视角，对已发表的交互式问答系统研究成果进行了呈现与分析。我们还描述了围绕特定任务及社区提出的关键议题的发展趋势，从而揭示了学者未来的研究兴趣。本文工作还获得了一个汇聚本文献研究所有主要主题概览的GitHub页面支持：https://sisinflab.github.io/interactive-question-answering-systems-survey/