Conversational Question Answering: A Comprehensive Survey

Question answering (QA) systems are important for getting information from different types of data, including organized and unorganized content in everyday language. In the changing world of conversational artificial intelligence (AI), QA has expanded to include conversational question answering (CQA). CQA involves understanding context and having ongoing conversations to meet user information needs. Recent CQA research, using big datasets and pre-trained language models, shows a shift to multi-turn QA. This survey gives a detailed look at current CQA trends, focusing on the move from single-turn to multi-turn QA. By discussing key progress and challenges, the survey is a foundation for researchers, offering insights for more innovation in the dynamic CQA field. Recognizing CQA's importance in addressing user needs in various applications like customer support and interactions with IoT devices, the survey sets the stage for more research. It ensures CQA systems remain adaptable and effective for evolving conversations and user demands.

Keywords:

: Question answering , Conversational agents , Conversational machine reading comprehension , Knowledge base , Conversational AI