A Survey on Neural Machine Reading Comprehension
It provides a comprehensive overview for researchers in natural language processing, but is incremental as it synthesizes existing work without introducing new methods.
The paper surveys neural machine reading comprehension, reviewing how neural networks are used to build readers, analyzing classic models and their improvements, and identifying defects and future directions.
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