GRAM: Grammar-Based Refined-Label Representing Mechanism in the Hierarchical Semantic Parsing Task

Dinh-Truong Do, Minh-Phuong Nguyen, Le-Minh Nguyen

Abstract: In this study, we proposed an efficient method to improve the performance of the hierarchical semantic parsing task by strengthening the meaning representation of the label candidate set via inductive grammar. In particular, grammar was first synthesized from the logical representations of training annotated data. Then, the model utilizes it as additional structured information for all expression label predictions. The grammar was also used to prevent unpromising directions in the semantic parsing process dynamically. The experimental results on the three well-known semantic parsing datasets, TOP, TOPv2 (low-resource settings), and ATIS, showed that our proposed method work effectiveness, which achieved new state-of-the-art (SOTA) results on TOP and TOPv2 datasets, and competitive results on the ATIS dataset.