Machine reading comprehension (MRC) is a long-standing topic in natural
language processing (NLP). The MRC task aims to answer a question based on the
given context. Recently studies focus on multi-hop MRC which is a more
challenging extension of MRC, which to answer a question some disjoint pieces
of information across the context are required. Due to the complexity and
importance of multi-hop MRC, a large number of studies have been focused on
this topic in recent years, therefore, it is necessary and worth reviewing the
related literature. This study aims to investigate recent advances in the
multi-hop MRC approaches based on 31 studies from 2018 to 2022. In this regard,
first, the multi-hop MRC problem definition will be introduced, then 31 models
will be reviewed in detail with a strong focus on their multi-hop aspects. They
also will be categorized based on their main techniques. Finally, a fine-grain
comprehensive comparison of the models and techniques will be presented.

本文综述了近年来一系列关于基于 31 项研究的多段式机器阅读理解方法的探索和研究成果，着重介绍了问题定义、模型技术，以及模型和技术之间的优缺点比较。