mlsanityData to 14 Oct 2026

Domain Specific Question to SQL Conversion with Embedded Data Balancing Technique

arXiv 2504.08753 ↗First posted 28 Mar 2025cs.IR cs.CL cs.DBcited by 0

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Abstract (arXiv)

The rise of deep learning in natural language processing has fostered the creation of text to structured query language models composed of an encoder and a decoder. Researchers have experimented with various intermediate processing like schema linking, table type aware, value extract. To generate accurate SQL results for the user question. However error analysis performed on the failed cases on these systems shows, 29 percentage of the errors would be because the system was unable to understand the values expressed by the user in their question. This challenge affects the generation of accurate SQL queries, especially when dealing with domain-specific terms and specific value conditions, where traditional methods struggle to maintain consistency and precision. To overcome these obstacles, proposed two intermediations like implementing data balancing technique and over sampling domain-specific queries which would refine the model architecture to enhance value recognition and fine tuning the model for domain-specific questions. This proposed solution achieved 10.98 percentage improvement in accuracy of the model performance compared to the state of the art model tested on WikiSQL dataset. to convert the user question accurately to SQL queries. Applying oversampling technique on the domain-specific questions shown a significant improvement as compared with traditional approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 28 Mar 2025.

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21 of 39 references.

References · 21

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9 more
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