Improving the Consistency of Semantic Parsing in KBQA Through Knowledge Distillation

Knowledge base question answering (KBQA) is an important task that involves analyzing natural language questions and retrieving relevant answers from a knowledge base. To achieve this, Semantic Parsing (SP) is used to parse the question into a structured logical form, which is then executed to obtain the answer. Although different logical forms have unique advantages, existing methods only focus on a single logical form and do not consider the semantic consistency between different logical forms. In this paper, we address the issue of consistency in semantic parsing, which has not been explored before. We show that improving the semantic consistency between multiple logical forms can help increase the parsing performance. To address the consistency problem, we present a dynamic knowledge distillation framework for semantic parsing (DKD-SP). Our framework enables one logical form to learn some useful hidden knowledge from another, which improves the semantic consistency of different logical forms. Additionally, it dynamically adjusts the supervised weight of the hidden knowledge as the student model's ability changes. We evaluate our approach on the KQA Pro dataset, and our experimental results confirm its effectiveness. Our method improves the overall accuracy of the seven types of questions by 0.57%, with notable improvements in the accuracy of Qualifier, Compare, and Count questions. Furthermore, in the compositional generalization scenario, the overall accuracy improved by 4.02%. Our codes are publicly available on https://github.com/zjtfo/SP Consistency By KD.