Conversational Question Answering Systems: A Comprehensive Literature Review

One crucial research topic in deep learning and natural language processing is conversational question-answering systems. Question responding is a narrative job that necessitates knowledge of the context of the conversation. Conversational Question Answering(CQA) incorporates contextual knowledge, co-reference resolution, and passage comprehension, in contrast to single-turn machine reading comprehension tasks. Because of recently popular datasets, including the Conversational Question Answering Challenge (CoQA), Question Answering in Context(QuAC), and Community Question Answering(CQA) tasks, it has become slightly easy, and recent work has achieved competitive performance on both datasets. However, some challenges still need to be solved to make a more efficient conversational question-answering system. This Article surveyed various methods and models in conversational question-answering systems and discussed different datasets and some limitations regarding this application. This survey paper aims to help researchers who want to work in this direction.