  Multi-hop Question Answering (QA) requires the machine to answer complex
questions by finding scattering clues and reasoning from multiple documents.
Graph Network (GN) and Question Decomposition (QD) are two common approaches at
present. The former uses the "black-box" reasoning process to capture the
potential relationship between entities and sentences, thus achieving good
performance. At the same time, the latter provides a clear reasoning logical
route by decomposing multi-hop questions into simple single-hop sub-questions.
In this paper, we propose a novel method to complete multi-hop QA from the
perspective of Question Generation (QG). Specifically, we carefully design an
end-to-end QG module on the basis of a classical QA module, which could help
the model understand the context by asking inherently logical sub-questions,
thus inheriting interpretability from the QD-based method and showing superior
performance. Experiments on the HotpotQA dataset demonstrate that the
effectiveness of our proposed QG module, human evaluation further clarifies its
interpretability quantitatively, and thorough analysis shows that the QG module
could generate better sub-questions than QD methods in terms of fluency,
consistency, and diversity.
