# Machine Reading Comprehension: a Literature Review

arXiv: https://arxiv.org/abs/1907.01686 · HTML: https://mlsanity.com/paper/1907.01686/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [English Machine Reading Comprehension Datasets: A Survey](https://mlsanity.com/paper/2101.10421/) (2021)
- [Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.00731/) (2022)
- [Improving Question Answering Performance Using Knowledge Distillation and Active Learning](https://mlsanity.com/paper/2109.12662/) (2021)
- [Building a Question and Answer System for News Domain](https://mlsanity.com/paper/2105.05744/) (2021)

## Abstract (arXiv)

Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence. This article summarizes recent advances in MRC, mainly focusing on two aspects (i.e., corpus and techniques). The specific characteristics of various MRC corpus are listed and compared. The main ideas of some typical MRC techniques are also described.