%0 Journal Article %A JI Jianmin %A XIE Defeng %T Syntax-enhanced semantic parsing with syntax-aware representation %D 2021 %R 10.11772/j.issn.1001-9081.2020111863 %J Journal of Computer Applications %P 2489-2495 %V 41 %N 9 %X Syntactic information, which is syntactic structure relations or dependency relations between words of a complete sentence, is an important and effective reference in Natural Language Processing (NLP). The task of semantic parsing is to directly transform natural language sentences into semantically complete and computer-executable languages. In previous semantic parsing studies, there are few efforts on improving the efficiency of end-to-end semantic parsing by using syntactic information from input sources. To further improve the accuracy and efficiency of the end-to-end semantic parsing model, a semantic parsing method was proposed to utilize the source-side dependency relation information of syntax to improve the model efficiency. As the basic idea of the method, an end-to-end dependency relation parser was pre-trained firstly. Then, the middle representation of the parser was used as syntax-aware representation, which was spliced with the original word embedding representation to generate a new input embedding representation, and this obtained input embedding representation was used in the end-to-end semantic parsing model. Finally, the model fusion was carried out by the transductive fusion learning. In the experiments, the proposed model was compared with the baseline model Transformer and the related works in the past decade. Experimental results show that, on ATIS, GEO and JOBS datasets, the semantic parsing model integrating dependency syntax-aware representation and transductive fusion learning achieves the best accuracy of 89.1%, 90.7%, and 91.4% respectively, which exceeds the performance of the Transformer. It verifies the effectiveness of introducing the dependency relation information of syntax. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020111863