  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.
