Papers › Semantic Parsing with Syntax- and Table-Aware SQL Generation
Semantic Parsing with Syntax- and Table-Aware SQL Generation
Yibo Sun, Duyu Tang, Nan Duan, Jianshu ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou
We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results are incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question-SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | WikiSQL | STAMP+RL (Sun et al., 2018)+ | Exact Match Accuracy | 61.0 | #4 of 10 | Archive leaderboard | report |
| Code Generation | WikiSQL | STAMP+RL (Sun et al., 2018)+ | Execution Accuracy | 74.6 | #4 of 10 | Archive leaderboard | report |
| Code Generation | WikiSQL | STAMP (Sun et al., 2018)+ | Exact Match Accuracy | 60.7 | #5 of 10 | Archive leaderboard | report |
| Code Generation | WikiSQL | STAMP (Sun et al., 2018)+ | Execution Accuracy | 74.4 | #5 of 10 | Archive leaderboard | report |
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