A Survey on Machine Reading Comprehension Systems
It provides a structured overview for researchers in natural language processing, but it is incremental as it synthesizes existing work without introducing new methods.
This paper presents a comprehensive survey of machine reading comprehension systems, analyzing 241 papers from 2016 to 2020 to identify trends such as shifts from answer extraction to generation and the adoption of pre-trained embeddings.
Machine reading comprehension is a challenging task and hot topic in natural language processing. Its goal is to develop systems to answer the questions regarding a given context. In this paper, we present a comprehensive survey on different aspects of machine reading comprehension systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on 241 reviewed papers from 2016 to 2020. Our investigations demonstrate that the focus of research has changed in recent years from answer extraction to answer generation, from single to multi-document reading comprehension, and from learning from scratch to using pre-trained embeddings. We also discuss the popular datasets and the evaluation metrics in this field. The paper ends with investigating the most cited papers and their contributions.