# Hierarchical Neural Data Synthesis for Semantic Parsing

arXiv: https://arxiv.org/abs/2112.02212 · HTML: https://mlsanity.com/paper/2112.02212/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Placed in

- Synthetic data and self-improvement for LLMs / [LLM synthetic data generation](https://mlsanity.com/synth/llm-synthetic-data-generation/)

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [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)
- [GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing](https://mlsanity.com/paper/2009.13845/) (2020)
- [Good-Enough Compositional Data Augmentation](https://mlsanity.com/paper/1904.09545/) (2019)
- [LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations](https://mlsanity.com/paper/2106.01093/) (2021)
- [Data Recombination for Neural Semantic Parsing](https://mlsanity.com/paper/1606.03622/) (2016)
- [Tagged Back-Translation](https://mlsanity.com/paper/1906.06442/) (2019)
- [Grounded Adaptation for Zero-shot Executable Semantic Parsing](https://mlsanity.com/paper/2009.07396/) (2020)
- [Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training](https://mlsanity.com/paper/2012.10309/) (2020)
- [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020)
- [Optimizing Deeper Transformers on Small Datasets](https://mlsanity.com/paper/2012.15355/) (2020)
- [Learning to Synthesize Data for Semantic Parsing](https://mlsanity.com/paper/2104.05827/) (2021)
- [GP: Context-free Grammar Pre-training for Text-to-SQL Parsers](https://mlsanity.com/paper/2101.09901/) (2021)
- [Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface](https://mlsanity.com/paper/2106.04559/) (2021)

## Built on by (most cited first, 11 of 11)

- [OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale](https://mlsanity.com/paper/2503.02240/) (2025)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions](https://mlsanity.com/paper/2208.13629/) (2022)
- [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)
- [Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQL](https://mlsanity.com/paper/2511.10192/) (2025)
- [Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers](https://mlsanity.com/paper/2210.16613/) (2022)
- [REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Models](https://mlsanity.com/paper/2504.04363/) (2025)
- [EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesis](https://mlsanity.com/paper/2601.04875/) (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)

## Abstract (arXiv)

Semantic parsing datasets are expensive to collect. Moreover, even the questions pertinent to a given domain, which are the input of a semantic parsing system, might not be readily available, especially in cross-domain semantic parsing. This makes data augmentation even more challenging. Existing methods to synthesize new data use hand-crafted or induced rules, requiring substantial engineering effort and linguistic expertise to achieve good coverage and precision, which limits the scalability. In this work, we propose a purely neural approach of data augmentation for semantic parsing that completely removes the need for grammar engineering while achieving higher semantic parsing accuracy. Furthermore, our method can synthesize in the zero-shot setting, where only a new domain schema is available without any input-output examples of the new domain. On the Spider cross-domain text-to-SQL semantic parsing benchmark, we achieve the state-of-the-art performance on the development set (77.2% accuracy) using our zero-shot augmentation.