Multi-hop Question Answering (MHQA) is the task of answering natural language questions that involve extracting and combining multiple pieces of information and doing multiple steps of reasoning. The ability to answer multi-hop questions and perform multi-step reasoning can significantly improve the utility of NLP systems. But the notion of 'multiple hops' is somewhat abstract which results in a large variety of tasks that require multi-hop reasoning. This leads to different datasets and models that differ significantly from each other and makes the field challenging to generalize and survey. In this monograph, the authors provide a general and formal definition of the MHQA task, and organize and summarize existing MHQA frameworks. They also outline some best practices for building MHQA datasets.
This monograph provides a systematic and thorough introduction to Multi-Hop Question Answering that is becoming increasingly important in practical AI systems.
