mlsanityData to 7 Oct 2026

Hierarchical Neural Data Synthesis for Semantic Parsing

arXiv 2112.02212 ↗First posted 4 Dec 2021cs.CLcited by 11

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

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.

Key facts. Cited by 11 papers in the map (data through 7 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.

Builds on

17 of 21 references.

References · 17

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  3. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  5. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
  6. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  7. 2019Good-Enough Compositional Data Augmentationcited by 158
  8. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  9. 2016Data Recombination for Neural Semantic Parsingcited by 111
  10. 2019Tagged Back-Translationcited by 76
  11. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  12. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
5 more
  1. 2020Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedbackcited by 44
  2. 2020Optimizing Deeper Transformers on Small Datasetscited by 41
  3. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  4. 2021GP: Context-free Grammar Pre-training for Text-to-SQL Parserscited by 5
  5. 2021Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interfacecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 3; 2026: 3.

11 papers.

  1. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
  2. 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
  3. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  4. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
  5. 2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
  6. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  7. 2022Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parserscited by 7
  8. 2025REFORMER: A ChatGPT-Driven Data Synthesis Framework Elevating Text-to-SQL Modelscited by 1
  9. 2026EvolSQL: Structure-Aware Evolution for Scalable Text-to-SQL Data Synthesiscited by 1
  10. 2026SemanticAgent: A Semantics-Aware Framework for Text-to-SQL Data Synthesiscited by 0
  11. 2026Knowledge Distillation for Low-Resource Open-source Text-to-SQL Modelcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data
  2. 2019Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
  3. 2024Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning
  4. 2020An Imitation Game for Learning Semantic Parsers from User Interaction
  5. 2022Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsing
  6. 2022XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing
  7. 2024Retrieval-Augmented Semantic Parsing: Improving Generalization with Lexical Knowledge
  8. 2024A synthetic data approach for domain generalization of NLI models