Multi-hop reasoning requires aggregating multiple documents to answer a
complex question. Existing methods usually decompose the multi-hop question
into simpler single-hop questions to solve the problem for illustrating the
explainable reasoning process. However, they ignore grounding on the supporting
facts of each reasoning step, which tends to generate inaccurate
decompositions. In this paper, we propose an interpretable stepwise reasoning
framework to incorporate both single-hop supporting sentence identification and
single-hop question generation at each intermediate step, and utilize the
inference of the current hop for the next until reasoning out the final result.
We employ a unified reader model for both intermediate hop reasoning and final
hop inference and adopt joint optimization for more accurate and robust
multi-hop reasoning. We conduct experiments on two benchmark datasets HotpotQA
and 2WikiMultiHopQA. The results show that our method can effectively boost
performance and also yields a better interpretable reasoning process without
decomposition supervision.

本文提出了一个解释性逐步推理框架，将单跳支持句子识别和单跳问题生成结合起来，并利用当前跳的推理来推导出最终结果。我们使用一个统一的读者模型进行中间跳推理和最终跳推理，并采用联合优化来实现更精确和稳健的多跳推理，取得了良好的实验效果。