## 5 Experiment
### Ablation Experiments  
RGISQL consists of four major components: refined grammatical information, reduced coupling, segmentation processing, and the RAT layer. To assess the influence of these components on model performance, each component was removed or replaced one at a time, and the performance was compared with that of the original model. The ablation experiments were conducted on the development set. Through ablation experiments on Spider, as shown in Figure 10, our results indicate that the proposed method outperformed traditional methods in several aspects. Firstly, removing the refined grammatical information led to a significant drop in performance, highlighting the importance of detailed syntactic analysis in understanding natural language questions and generating accurate SQL statements. This advantage enables our model to perform better when dealing with complex queries. Secondly, removing components designed to reduce the coupling of edge embeddings also resulted in a decline in performance, indicating that our strategy effectively simplifies the complexity of edge embeddings in graph neural networks, thereby enhancing learning efficiency. The effectiveness of the segmentation strategy for long queries has been validated, and compared with other methods, our model demonstrates superior robustness and generalization capabilities. Lastly, removing components related to the RAT layers also led to a drop in performance, emphasizing the key role of unifying node and edge embedding representations in enhancing overall performance. The experiments were also conducted on Spider-Syn, as shown in Figure 11. RGISQL demonstrated significant advantages in the task of text-to-SQL conversion, particularly in its ability to handle complex and lengthy queries, thereby providing a more effective solution in this field. \begin{table}
\begin{tabular}{l c} \hline \hline
**Models** & **Acc** \\ \hline Global-GNN [18] & 23.6 \\ IRNet [24] & 28.4 \\ RATSQL+Grappa [7] & 49.1 \\ S\({}^{2}\)SQL+Grappa [30] & 51.4 \\
**RGISQL+Grappa** & **52.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The exact set match accuracy of different methods on the Spider-Syn dev and test sets. Figure 10: Ablation results on Spider. Among them, (**a**) represents the results of exact set matching accuracy, and (**b**) represents the results of execution accuracy. “RGI” stands for refined grammatical information. “RC” stands for reduced coupling. “SEG” refers to segmentation processing. “EM” refers to exact set match accuracy. “EX” refers to execution accuracy. Additionally, to explore the impact of embedding methods generated by pre-trained language models on various models, RGISQL was compared with two baseline models that utilize RoBERTa. The results are presented in Table 7. It is evident that regardless of the model used to generate embeddings, RGISQL's performance was significantly superior to that of the other models. This finding indicates that our method does not rely solely on the characteristics of a single model but demonstrates broad adaptability and robustness. In Spider, we conducted ablation experiments to evaluate the use of ASTormer and the approach of combining node-centric and edge-centric graphs to update embeddings, with the results shown in Figure 12. The experiments demonstrate that combining these two graph structures effectively enhances the model's ability to handle complex relationships, resulting in more accurate SQL queries. The ASTormer decoder enhances SQL generation accuracy by effectively capturing the dependencies between nodes and edges. This multi-relational graph structure demonstrates greater robustness compared with traditional methods, particularly in processing complex natural language queries, where the model excels. Furthermore, the correlation between different words in natural language questions was calculated.