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.

翻译：多动推理要求将多个文件集中在一起回答复杂问题。 现有方法通常将多动问题分解成简单的单动问题,以解决说明可解释推理过程的问题。 但是,它们忽略了每个推理步骤的佐证事实,而每个推理步骤往往会产生不准确的分解。 在本文件中,我们提出了一个可解释的分步推理框架,将单动支持句识别和单跳问题生成都纳入每个中间步骤,并在解释最后结果之前将当前跳动的推理用于下一个阶段。 我们采用了中间跳推理和最后跳推理的统一阅读模型,并采用了联合优化,以进行更准确和有力的多动推理。 我们在两个基准数据集HotpotQA和2WikiMultiHopA上进行了实验。 结果表明,我们的方法可以有效提升业绩,并在不进行分解监督的情况下实现更好的解释推理过程。