A Survey on Neural Machine Reading Comprehension

TL;DR

This survey reviews neural network-based models for machine reading comprehension, highlighting their developments, limitations, and future research directions in leveraging deep learning for understanding natural language texts.

Contribution

It provides a comprehensive overview of neural models for MRC, analyzing their improvements, shortcomings, and suggesting future research avenues.

Findings

Neural models have significantly advanced MRC performance.

Current models still face challenges like reasoning and understanding complex language.

The paper identifies key areas for future research in neural MRC.

Abstract

Enabling a machine to read and comprehend the natural language documents so that it can answer some questions remains an elusive challenge. In recent years, the popularity of deep learning and the establishment of large-scale datasets have both promoted the prosperity of Machine Reading Comprehension. This paper aims to present how to utilize the Neural Network to build a Reader and introduce some classic models, analyze what improvements they make. Further, we also point out the defects of existing models and future research directions