Question answering (QA) systems provide a way of querying the information
available in various formats including, but not limited to, unstructured and
structured data in natural languages. It constitutes a considerable part of
conversational artificial intelligence (AI) which has led to the introduction
of a special research topic on Conversational Question Answering (CQA), wherein
a system is required to understand the given context and then engages in
multi-turn QA to satisfy the user's information needs. Whilst the focus of most
of the existing research work is subjected to single-turn QA, the field of
multi-turn QA has recently grasped attention and prominence owing to the
availability of large-scale, multi-turn QA datasets and the development of
pre-trained language models. With a good amount of models and research papers
adding to the literature every year recently, there is a dire need of arranging
and presenting the related work in a unified manner to streamline future
research. This survey, therefore, is an effort to present a comprehensive
review of the state-of-the-art research trends of CQA primarily based on
reviewed papers from 2016-2021. Our findings show that there has been a trend
shift from single-turn to multi-turn QA which empowers the field of
Conversational AI from different perspectives. This survey is intended to
provide an epitome for the research community with the hope of laying a strong
foundation for the field of CQA.

本文对 2016 至 2021 年所发表关于问答系统的各项研究进行了回顾与总结，发现多轮问答系统已取代了单轮问答系统的先前主导地位，这表明了其在提高人工智能对话系统方面的重要性，同时也描绘了开展更多进一步和有利的研究的未来研究方向。