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It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on <jats:italic>conversational question answering<\/jats:italic> (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user\u2019s information needs. While 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 is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. 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.\n<\/jats:p>","DOI":"10.1007\/s10115-022-01744-y","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T04:04:47Z","timestamp":1662437087000},"page":"3151-3195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Conversational question answering: a survey"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2394-0874","authenticated-orcid":false,"given":"Munazza","family":"Zaib","sequence":"first","affiliation":[]},{"given":"Wei Emma","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[]},{"given":"Adnan","family":"Mahmood","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"1744_CR1","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. 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