Conversational Machine Comprehension (CMC) is a research track in conversational AI which expects the machine to understand an open-domain  text and thereafter engage in a multi-turn conversation to answer questions related to the text. While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering  , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models like BERT and the introduction of large-scale conversational datasets like CoQA and QuAC. The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature. With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research. This literature review , therefore, is a first-of-its-kind attempt at providing a holistic overview of CMC , with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history. It focuses on synthesizing a generic framework for CMC models , rather than describing the models individually. The review is intended to serve as a compendium  for future researchers in this domain .
Conversational Machine Comprehension (CMC) , a research track in conversational AI , expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA) , multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC. The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature. With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research. This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history. The review synthesizes a generic framework for CMC models while highlighting the differences in recent approaches and intends to serve as a compendium of CMC for future researchers .
R
is
,
R
which
,
A
natural language
A
(QA)
R
like
such as
R
like
such as
R
, therefore, is a first-of-its-kind attempt
attempts
D
,
R
It focuses on synthesizing
The review synthesizes
R
, rather than describing the models individually. The review is intended
while highlighting the differences in recent approaches and intends
A
of CMC
D
in this domain