Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. However, integrating information from heterogeneous knowledge sources is essential to answer complex questions. In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. To facilitate reasoning, we propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT). First, we build the Hierarchical Question Decomposition Tree (HQDT) to understand the semantics of a complex question; then, we conduct probabilistic reasoning over HQDT from root to leaves recursively, to aggregate heterogeneous knowledge at different tree levels and search for a best solution considering the decomposing and answering probabilities. The experiments on complex QA datasets KQA Pro and Musique show that our framework outperforms SOTA methods significantly, demonstrating the effectiveness of leveraging question decomposing for knowledge integration and our RoHT framework.

翻译：可解释问答旨在回答给定问题，并提供解释说明为何选择该答案。现有可解释问答方法仅关注在单一知识源（如结构化知识库、非结构化语料库等）上进行推理。然而，整合异构知识源的信息对回答复杂问题至关重要。本文提出利用问题分解法实现异构知识整合，通过将复杂问题分解为更简单的子问题，并为每个子问题选择适当的推理知识源。为促进推理过程，我们提出一种新颖的两阶段可解释问答框架——基于层次化问题分解树的推理（RoHT）。首先构建层次化问题分解树（HQDT）以理解复杂问题的语义；随后从根节点到叶子节点递归进行概率推理，在不同树层级聚合异构知识，并综合分解概率与回答概率搜索最优解。在复杂问答数据集KQA Pro和Musique上的实验表明，本框架显著优于当前最优方法，验证了利用问题分解实现知识整合的有效性以及RoHT框架的优越性。