# Neural Machine Reading Comprehension: Methods and Trends

## TL;DR

This paper provides a comprehensive review of neural machine reading comprehension, covering key tasks, architectures, recent trends, challenges, and future directions in the field.

## Contribution

It offers the first thorough survey summarizing existing approaches, datasets, architectures, and emerging trends in neural MRC research.

## Key findings

- Summarizes various MRC tasks and datasets
- Analyzes common neural MRC architectures and modules
- Discusses emerging trends and open challenges

## Abstract

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences, and representative datasets; (2) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.