Parsing questions into executable logical forms has showed impressive results for knowledge-base question answering (KBQA). However, complex KBQA is a more challenging task that requires to perform complex multi-step reasoning. Recently, a new semantic parser called KoPL has been proposed to explicitly model the reasoning processes, which achieved the state-of-the-art on complex KBQA. In this paper, we further explore how to unlock the reasoning ability of semantic parsers by a simple proposed parse-execute-refine paradigm. We refine and improve the KoPL parser by demonstrating the executed intermediate reasoning steps to the KBQA model. We show that such simple strategy can significantly improve the ability of complex reasoning. Specifically, we propose three components: a parsing stage, an execution stage and a refinement stage, to enhance the ability of complex reasoning. The parser uses the KoPL to generate the transparent logical forms. Then, the execution stage aligns and executes the logical forms over knowledge base to obtain intermediate reasoning processes. Finally, the intermediate step-by-step reasoning processes are demonstrated to the KBQA model in the refinement stage. With the explicit reasoning processes, it is much easier to answer the complex questions. Experiments on benchmark dataset shows that the proposed PER-KBQA performs significantly better than the stage-of-the-art baselines on the complex KBQA.

翻译：将问题解析为可执行逻辑形式在知识库问答中已展现出显著成效。然而，复杂知识库问答需要执行复杂的多步推理，是一项更具挑战性的任务。近期提出的新型语义解析器KoPL通过显式建模推理过程，已在复杂知识库问答中达到最优水平。本文进一步探索如何通过简单提出的"解析-执行-精炼"范式释放语义解析器的推理能力。我们通过向知识库问答模型展示已执行的中间推理步骤来改进KoPL解析器。研究表明，这种简单策略能显著提升复杂推理能力。具体而言，我们提出三个组件：解析阶段、执行阶段和精炼阶段，以增强复杂推理能力。解析器利用KoPL生成透明的逻辑形式；执行阶段将逻辑形式与知识库对齐并执行，获取中间推理过程；最后在精炼阶段将这些逐步推理过程呈现给知识库问答模型。借助显式推理过程，复杂问题的答案获取变得更加便捷。基准数据集实验表明，所提出的PER-KBQA在复杂知识库问答任务上显著优于当前最优基线模型。