  Multi-hop QA requires the machine to answer complex questions through finding
multiple clues and reasoning, and provide explanatory evidence to demonstrate
the machine reasoning process. We propose Relation Extractor-Reader and
Comparator (RERC), a three-stage framework based on complex question
decomposition, which is the first work that the RERC model has been proposed
and applied in solving the multi-hop QA challenges. The Relation Extractor
decomposes the complex question, and then the Reader answers the sub-questions
in turn, and finally the Comparator performs numerical comparison and
summarizes all to get the final answer, where the entire process itself
constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset,
our RERC model has achieved the most advanced performance, with a winning joint
F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to
human performance, with only 1.95 behind the human level in F1 score of support
fact. At the same time, the evidence path provided by our RERC framework has
excellent readability and faithfulness.
