# Question Generation from SQL Queries Improves Neural Semantic Parsing

arXiv: https://arxiv.org/abs/1808.06304 · HTML: https://mlsanity.com/paper/1808.06304/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 29 of 29)

- [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://mlsanity.com/paper/2503.02240/) (2025)
- [Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect](https://mlsanity.com/paper/2208.10099/) (2022)
- [ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems](https://mlsanity.com/paper/2306.04743/) (2023)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [Importance of Synthesizing High-quality Data for Text-to-SQL Parsing](https://mlsanity.com/paper/2212.08785/) (2022)
- [Selective Demonstrations for Cross-domain Text-to-SQL](https://mlsanity.com/paper/2310.06302/) (2023)
- [SQL-GEN: Bridging the Dialect Gap for Text-to-SQL Via Synthetic Data And Model Merging](https://mlsanity.com/paper/2408.12733/) (2024)
- [On Robustness of Neural Semantic Parsers](https://mlsanity.com/paper/2102.01563/) (2021)
- [From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding](https://mlsanity.com/paper/2106.06228/) (2021)
- [On The Ingredients of an Effective Zero-shot Semantic Parser](https://mlsanity.com/paper/2110.08381/) (2021)
- [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://mlsanity.com/paper/2511.10192/) (2025)
- [Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play](https://mlsanity.com/paper/2210.12096/) (2022)
- [Topic Transferable Table Question Answering](https://mlsanity.com/paper/2109.07377/) (2021)
- [Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers](https://mlsanity.com/paper/2210.16613/) (2022)
- [Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework](https://mlsanity.com/paper/2212.10097/) (2022)
- [Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotation](https://mlsanity.com/paper/2502.15980/) (2025)
- [QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL](https://mlsanity.com/paper/2406.10593/) (2024)
- [Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization](https://mlsanity.com/paper/2303.08697/) (2023)
- [Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation](https://mlsanity.com/paper/2406.03703/) (2024)
- [Controllable Data Augmentation for Context-Dependent Text-to-SQL](https://mlsanity.com/paper/2304.13902/) (2023)
- [REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Models](https://mlsanity.com/paper/2504.04363/) (2025)
- [SQL-Exchange: Transforming SQL Queries Across Domains](https://mlsanity.com/paper/2508.07087/) (2025)
- [EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2601.04875/) (2026)
- [Domain Specific Question to SQL Conversion with Embedded Data Balancing Technique](https://mlsanity.com/paper/2504.08753/) (2025)
- [GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models](https://mlsanity.com/paper/2509.01308/) (2025)
- [RingSQL: Schema-Independent Synthetic Data Generation for Text-to-SQL Reinforcement Learning](https://mlsanity.com/paper/2601.05451/) (2026)
- [SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2604.21414/) (2026)
- [Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model](https://mlsanity.com/paper/2605.22843/) (2026)
- [COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training](https://mlsanity.com/paper/2609.20842/) (2026)

## Abstract (arXiv)

We study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.