# Domain Specific Question to SQL Conversion with Embedded Data Balancing Technique

arXiv: https://arxiv.org/abs/2504.08753 · HTML: https://mlsanity.com/paper/2504.08753/

## 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.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers](https://mlsanity.com/paper/1911.04942/) (2019)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning](https://mlsanity.com/paper/1711.04436/) (2017)
- [TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation](https://mlsanity.com/paper/1804.09769/) (2018)
- [Text-to-SQL Generation for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1908.01839/) (2019)
- [Grounded Adaptation for Zero-shot Executable Semantic Parsing](https://mlsanity.com/paper/2009.07396/) (2020)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases](https://mlsanity.com/paper/2004.03125/) (2020)
- [Learning to Synthesize Data for Semantic Parsing](https://mlsanity.com/paper/2104.05827/) (2021)
- [Hybrid Ranking Network for Text-to-SQL](https://mlsanity.com/paper/2008.04759/) (2020)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [Question Generation from SQL Queries Improves Neural Semantic Parsing](https://mlsanity.com/paper/1808.06304/) (2018)
- [SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising](https://mlsanity.com/paper/2105.07911/) (2021)
- [Data Agnostic RoBERTa-based Natural Language to SQL Query Generation](https://mlsanity.com/paper/2010.05243/) (2020)

## 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.