  Obtaining training data for multi-hop question answering (QA) is
time-consuming and resource-intensive. We explore the possibility to train a
well-performed multi-hop QA model without referencing any human-labeled
multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose
MQA-QG, an unsupervised framework that can generate human-like multi-hop
training data from both homogeneous and heterogeneous data sources. MQA-QG
generates questions by first selecting/generating relevant information from
each data source and then integrating the multiple information to form a
multi-hop question. Using only generated training data, we can train a
competent multi-hop QA which achieves 61% and 83% of the supervised learning
performance for the HybridQA and the HotpotQA dataset, respectively. We also
show that pretraining the QA system with the generated data would greatly
reduce the demand for human-annotated training data. Our codes are publicly
available at https://github.com/teacherpeterpan/Unsupervised-Multi-hop-QA.
