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.
翻译：机器阅读理解(MRC)是自然语言处理(NLP)中长期存在的一个专题。MRC的任务是根据特定背景回答一个问题。最近研究的重点是多霍MRC,这是MRC的更具有挑战性的延伸,对于一个问题,需要从全局回答一些脱节的信息。由于多霍MRC的复杂性和重要性,近年来大量研究侧重于这个专题,因此,有必要也值得审查相关文献。这项研究的目的是根据2018年至2022年的31项研究,调查多霍MRC方法的最新进展。在这方面,首先,将引入多霍MRC问题定义,然后对31个模型进行详细审查,重点将放在其多霍特方面。还将根据它们的主要技术对其进行分类。最后,将对模型和技术进行精细的全面比较。
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