  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
