# Semantic Parsing with Syntax- and Table-Aware SQL Generation

arXiv: https://arxiv.org/abs/1804.08338 · HTML: https://mlsanity.com/paper/1804.08338/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 17 of 17)

- [A Survey on Table Question Answering: Recent Advances](https://mlsanity.com/paper/2207.05270/) (2022)
- [Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification](https://mlsanity.com/paper/2303.08006/) (2023)
- [Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing](https://mlsanity.com/paper/2103.02227/) (2021)
- [Improving Text-to-SQL with Schema Dependency Learning](https://mlsanity.com/paper/2103.04399/) (2021)
- [UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL](https://mlsanity.com/paper/2203.07781/) (2022)
- [Understanding, Detecting, and Repairing Real-World In-Context-Learning-Based Text-to-SQL Errors](https://mlsanity.com/paper/2501.09310/) (2025)
- [Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks](https://mlsanity.com/paper/2310.00789/) (2023)
- [Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://mlsanity.com/paper/2307.16013/) (2023)
- [End-to-end Text-to-SQL Generation within an Analytics Insight Engine](https://mlsanity.com/paper/2406.12104/) (2024)
- [Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question](https://mlsanity.com/paper/2201.01209/) (2022)
- [Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL](https://mlsanity.com/paper/2112.08735/) (2021)
- [On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software](https://mlsanity.com/paper/2311.11979/) (2023)
- [ERATTA: Extreme RAG for Table To Answers with Large Language Models](https://mlsanity.com/paper/2405.03963/) (2024)
- [JudgeSQL: Reasoning over SQL Candidates with Weighted Consensus Tournament](https://mlsanity.com/paper/2510.15560/) (2025)
- [EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2601.04875/) (2026)
- [Self-supervised Text-to-SQL Learning with Header Alignment Training](https://mlsanity.com/paper/2103.06402/) (2021)
- [Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling](https://mlsanity.com/paper/2306.17646/) (2023)

## Abstract (arXiv)

We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results are incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question-SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.