Conversational Machine Comprehension: a Literature Review

• URL: http://arxiv.org/abs/2006.00671v2
• Date: Thu, 5 Nov 2020 06:28:06 GMT
• Title: Conversational Machine Comprehension: a Literature Review
• Authors: Somil Gupta, Bhanu Pratap Singh Rawat, Hong Yu
• Abstract summary: Conversational Machine (CMC) is a research track in conversational AI. It expects the machine to understand an open-domain natural language text and engage in a multi-turn conversation to answer questions related to the text. This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models.
• Score: 5.857042938931491
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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