Conversational machine comprehension: a literature review. Gupta, S., Rawat, B. P. S., & Yu, H. arXiv preprint arXiv:2006.00671, December, 2020. COLING 2020![Conversational machine comprehension: a literature review [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper 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},
url = {https://aclanthology.org/2020.coling-main.247},
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},
note = {COLING 2020},
pages = {2739--2753},
}
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