TY  - JOUR
T1  - A Text-to-SQL Model with Selective Decoding
AU  - Han, Mirae 
AU  - Jeong, Geunyeong 
AU  - Kim, Harksoo 
JO  - Journal of KIISE, JOK
PY  - 2025
DA  - 2025/1/14
DO  - 10.5626/JOK.2025.52.11.907
KW  - semantic parsing
KW  - Text-to-SQL
KW  - pointer network
KW  - decoding
AB  - 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.