Papers › SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL
Ruichu Cai, Jinjie Yuan, Boyan Xu, Zhifeng Hao
The Text-to-SQL task, aiming to translate the natural language of the questions into SQL queries, has drawn much attention recently. One of the most challenging problems of Text-to-SQL is how to generalize the trained model to the unseen database schemas, also known as the cross-domain Text-to-SQL task. The key lies in the generalizability of (i) the encoding method to model the question and the database schema and (ii) the question-schema linking method to learn the mapping between words in the question and tables/columns in the database schema. Focusing on the above two key issues, we propose a Structure-Aware Dual Graph Aggregation Network (SADGA) for cross-domain Text-to-SQL. In SADGA, we adopt the graph structure to provide a unified encoding model for both the natural language question and database schema. Based on the proposed unified modeling, we further devise a structure-aware aggregation method to learn the mapping between the question-graph and schema-graph. The structure-aware aggregation method is featured with Global Graph Linking, Local Graph Linking, and Dual-Graph Aggregation Mechanism. We not only study the performance of our proposal empirically but also achieved 3rd place on the challenging Text-to-SQL benchmark Spider at the time of writing.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Parsing | spider | SADGA + GAP | Accuracy | 70.1 | #6 of 10 | Archive leaderboard | report |
| Text-To-SQL | spider | SADGA + GAP | Exact Match Accuracy (Dev) | 73.1 | #13 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | SADGA + GAP | Exact Match Accuracy (Test) | 70.1 | #13 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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