Conversational machine comprehension: a literature review. Gupta, S., Rawat, B. P. S., & Yu, H. arXiv preprint arXiv:2006.00671, December, 2020.
doi  abstract   bibtex   
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.
@article{gupta_conversational_2020,
	title = {Conversational machine comprehension: a literature review},
	shorttitle = {Conversational machine comprehension},
	doi = {10.18653/v1/2020.coling-main.247},
	abstract = {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.},
	journal = {arXiv preprint arXiv:2006.00671},
	author = {Gupta, Somil and Rawat, Bhanu Pratap Singh and Yu, Hong},
	month = dec,
	year = {2020},
	pages = {2739--2753},
}

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