Mirae Han  Geunyeong Jeong  Harksoo Kim

10.5626/JOK.2025.52.11.907

semantic parsing Text-to-SQL Pointer Network Decoding

Text-to-SQL involves converting natural language questions into SQL queries. Existing models primarily use either sketch-based or generation-based approaches. However, sketch-based methods struggle to fully capture the relationships among SQL elements, while generation-based methods suffer from slow inference speeds and frequent syntax errors. To address these challenges, this paper proposes a new decoding strategy called Selective Decoding. This approach combines the strengths of both methods by utilizing sketch structure and selectively applying the most suitable decoding method at each step. As a result, the model effectively captures the interrelationships among SQL elements and generate syntactically correct SQL queries. Experimental results demonstrate that the proposed model generates SQL queries more efficiently and accurately than existing models.