  Effective multi-hop question answering (QA) requires reasoning over multiple
scattered paragraphs and providing explanations for answers. Most existing
approaches cannot provide an interpretable reasoning process to illustrate how
these models arrive at an answer. In this paper, we propose a Question
Decomposition method based on Abstract Meaning Representation (QDAMR) for
multi-hop QA, which achieves interpretable reasoning by decomposing a multi-hop
question into simpler sub-questions and answering them in order. Since
annotating the decomposition is expensive, we first delegate the complexity of
understanding the multi-hop question to an AMR parser. We then achieve the
decomposition of a multi-hop question via segmentation of the corresponding AMR
graph based on the required reasoning type. Finally, we generate sub-questions
using an AMR-to-Text generation model and answer them with an off-the-shelf QA
model. Experimental results on HotpotQA demonstrate that our approach is
competitive for interpretable reasoning and that the sub-questions generated by
QDAMR are well-formed, outperforming existing question-decomposition-based
multi-hop QA approaches.
