Interactive Question Answering Systems: Literature Review

Abstract

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/

37 pages, 2 Figures, 6 Tables, just accepted

References in corpus (16)

• Gemini: A Family of Highly Capable Multimodal Models
• Large-scale Simple Question Answering with Memory Networks
• Quasar: Datasets for Question Answering by Search and Reading
• SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
• Explaining Question Answering Models through Text Generation
• Technical report on Conversational Question Answering
• Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
• DeepStory: Video Story QA by Deep Embedded Memory Networks
• Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
• An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering
• Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer
• Exploring BERT Parameter Efficiency on the Stanford Question Answering Dataset v2.0
• Multimodal Conversational AI: A Survey of Datasets and Approaches
• Few-shot In-context Learning for Knowledge Base Question Answering
• Learning to Explain: Answering Why-Questions via Rephrasing
• Gendered Pronoun Resolution using BERT and an extractive question answering formulation