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.

本文提出了一种新的两阶段可解释问答框架 RoHT，该框架借助问题分解进行异构知识的集成，从而有效地回答复杂问题，并在 KQA Pro 和 Musique 等数据集上显著优于现有技术，证明了问题分解对知识集成和 RoHT 框架的有效性。