Related papers: Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering

URL: http://arxiv.org/abs/2305.15056v1
Date: Wed, 24 May 2023 11:45:59 GMT
Title: Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering
Authors: Jiajie Zhang, Shulin Cao, Tingjia Zhang, Xin Lv, Jiaxin Shi, Qi Tian, Juanzi Li, Lei Hou
Abstract summary: We propose to leverage question decomposing for heterogeneous knowledge integration. We propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT) Experiments on complex QA datasets KQA Pro and Musique show that our framework outperforms SOTA methods significantly.
Score: 83.74210749046551
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.