Improving Core Path Reasoning for the Weakly Supervised Knowledge Base Question Answering

Abstract

Core Path Reasoning (CPR) is an essential part of the knowledge base question answering (KBQA), which determines whether the answer can be found correctly and indicates the reasonableness of the path. The lack of effective supervision of the core path in weakly supervised KBQA faces great challenges in finding the correct answer through long core paths. Furthermore, even if the correct answer is found, its path might be spurious that is not semantically relevant to the question. In this paper, we focus on solving the CPR problem in weakly supervised KBQA. We introduce a CPR model that aligns questions and paths in a step-by-step reasoning manner from explicit text semantic matching and implicit knowledge bases structure matching. Additionally, we propose a two-stage learning strategy to alleviate the spurious path problem efficiently. We first find relatively correct paths and then use hard Expectation-Maximization to learn the best matching path iteratively. Extensive experiments on two popular KBQA datasets demonstrate the strong competitiveness of our model compared to previous state-of-the-art methods, especially in solving long path and spurious path problem.