The task of Question Answering (QA) has attracted significant research interest for a long time. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting, makes the task of QA crucial for strong AI systems. Recent success on simple QA tasks has shifted the focus to more complex settings. Among these, Multi-Hop QA (MHQA) is one of the most researched tasks over recent years. In broad terms, MHQA is the task of answering natural language questions that involve extracting and combining multiple pieces of information and doing multiple steps of reasoning. An example of a multi-hop question would be “The Argentine PGA Championship record holder has won how many tournaments worldwide?”. Answering the question would need two pieces of information: “Who is the record holder for Argentine PGA Championship tournaments?” and “How many tournaments did [Answer of Sub Q1] win?”. The ability to answer multi-hop questions and perform multi step reasoning can significantly improve the utility of NLP systems. Consequently, the field has seen a surge of high quality datasets, models and evaluation strategies. The notion of ‘multiple hops’ is somewhat abstract which results in a large variety of tasks that require multihop reasoning. This leads to different datasets and models that differ significantly from each other and make the field challenging to generalize and survey. We aim to provide a general and formal definition of the MHQA task, and organize and summarize existing MHQA frameworks. We also outline some best practices for building MHQA datasets. This monograph provides a systematic and thorough introduction as well as the structuring of the existing attempts to this highly interesting, yet quite challenging task.

问答（QA）任务长期以来引起了人们的广泛研究兴趣。它与语言理解和知识检索任务的相关性以及简单的设置使得 QA 任务对于强大的人工智能系统至关重要。最近在简单的 QA 任务上取得的成功已将重点转移到更复杂的设置上。其中，多跳质量保证（MHQA）是近年来研究最多的任务之一。从广义上讲，MHQA 是回答自然语言问题的任务，涉及提取和组合多条信息并进行多个推理步骤。多跳问题的一个例子是“阿根廷 PGA 锦标赛纪录保持者赢得了全球多少场比赛？”。回答这个问题需要两条信息：“谁是阿根廷 PGA 锦标赛的纪录保持者？”和“[子问题 1 的答案]赢得了多少场比赛？”。回答多跳问题和执行多步推理的能力可以显着提高 NLP 系统的实用性。因此，该领域出现了大量高质量的数据集、模型和评估策略。 “多跳”的概念有些抽象，这导致需要多跳推理的任务种类繁多。这导致不同的数据集和模型彼此之间存在显着差异，并使该领域难以概括和调查。我们的目标是提供 MHQA 任务的通用且正式的定义，并组织和总结现有的 MHQA 框架。我们还概述了构建 MHQA 数据集的一些最佳实践。本专着系统而全面地介绍了这一非常有趣但又颇具挑战性的任务的现有尝试的结构。