论文标题
找到然后询问：多跳问题回答的可解释的逐步推理
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering
论文作者
论文摘要
多跳的推理需要汇总多个文档来回答一个复杂的问题。现有方法通常将多跳问题分解为更简单的单跳问题，以解决说明可解释的推理过程的问题。但是，他们忽略了每个推理步骤的支持事实的基础，这往往会产生不准确的分解。在本文中，我们提出了一个可解释的逐步推理框架，以在每个中间步骤中同时合并单跳支持句子识别和单跳问题生成，并利用当前跳跃的推断，直到推理最终结果。我们采用统一的读者模型来进行中级跳跃推理和最终的跳跃推理，并采用关节优化，以更准确，强大的多跳上推理。我们在两个基准数据集HotPotQA和2Wikimultihopqa上进行实验。结果表明，我们的方法可以有效地提高性能，并在不分解监督的情况下产生更好的解释推理过程。
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.
