# Conversational Machine Comprehension: a Literature Review

arXiv: https://arxiv.org/abs/2006.00671 · HTML: https://mlsanity.com/paper/2006.00671/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [RoR: Read-over-Read for Long Document Machine Reading Comprehension](https://mlsanity.com/paper/2109.04780/) (2021)
- [On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method](https://mlsanity.com/paper/2206.14796/) (2022)
- [Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts](https://mlsanity.com/paper/2105.01542/) (2021)
- [To Answer or Not to Answer? Improving Machine Reading Comprehension Model with Span-based Contrastive Learning](https://mlsanity.com/paper/2208.01299/) (2022)
- [A Survey of the State-of-the-Art in Conversational Question Answering Systems](https://mlsanity.com/paper/2509.05716/) (2025)
- [Unsupervised Technique To Conversational Machine Reading](https://mlsanity.com/paper/2106.15247/) (2021)
- [Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context](https://mlsanity.com/paper/2203.03244/) (2022)
- [Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering](https://mlsanity.com/paper/2212.08946/) (2022)

## Abstract (arXiv)

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.