mlsanityData to 14 Oct 2026

Weakly Supervised Text-to-SQL Parsing through Question Decomposition

arXiv 2112.06311 ↗First posted 12 Dec 2021cs.CL cs.AI cs.DBcited by 15

Not on the map. Why?

Abstract (arXiv)

Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we propose a weak supervision approach for training text-to-SQL parsers. We take advantage of the recently proposed question meaning representation called QDMR, an intermediate between NL and formal query languages. Given questions, their QDMR structures (annotated by non-experts or automatically predicted), and the answers, we are able to automatically synthesize SQL queries that are used to train text-to-SQL models. We test our approach by experimenting on five benchmark datasets. Our results show that the weakly supervised models perform competitively with those trained on annotated NL-SQL data. Overall, we effectively train text-to-SQL parsers, while using zero SQL annotations.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 24 of its 59 references that are in the map. First posted on arXiv 12 Dec 2021.

Builds on

24 of 59 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  4. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 687
  5. 2015Compositional Semantic Parsing on Semi-Structured Tablescited by 515
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 424
  7. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
  8. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  9. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  10. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  11. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  12. 2018Improving Text-to-SQL Evaluation Methodologycited by 135
12 more
  1. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  2. 2020Break It Down: A Question Understanding Benchmarkcited by 131
  3. 2020Structure-Grounded Pretraining for Text-to-SQLcited by 126
  4. 2018Coarse-to-Fine Decoding for Neural Semantic Parsingcited by 110
  5. 2021KaggleDBQA: Realistic Evaluation of Text-to-SQL Parserscited by 106
  6. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  7. 2021Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representationscited by 55
  8. 2019A Comparative Survey of Recent Natural Language Interfaces for Databasescited by 33
  9. 2019Learning Semantic Parsers from Denotations with Latent Structured Alignments and Abstract Programscited by 26
  10. 2020Obtaining Faithful Interpretations from Compositional Neural Networkscited by 23
  11. 2021Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decompositioncited by 11
  12. 2021SPARQLing Database Queries from Intermediate Question Decompositionscited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 3; 2026: 2.

15 papers.

Sort:

By area:

  1. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  2. 2024RSL-SQL: Robust Schema Linking in Text-to-SQL Generationcited by 52
  3. 2024MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQLcited by 22
  4. 2025Disambiguate First, Parse Later: Generating Interpretations for Ambiguity Resolution in Semantic Parsingcited by 13
  5. 2023Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasetscited by 9
  6. 2024Superlatives in Context: Modeling the Implicit Semantics of Superlativescited by 5
  7. 2023A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Textcited by 4
  8. 2024Generating Tables from the Parametric Knowledge of Language Modelscited by 3
  9. 2025Warmup Generations: A Task-Agnostic Approach for Guiding Sequence-to-Sequence Learning with Unsupervised Initial State Generationcited by 2
  10. 2023Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databasescited by 1
  11. 2024Semantic Parsing with Candidate Expressions for Knowledge Base Question Answeringcited by 1
  12. 2025Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarizationcited by 1
3 more
  1. 2026Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know"cited by 1
  2. 2022Searching for Better Database Queries in the Outputs of Semantic Parserscited by 0
  3. 2026CAPER: Clause-Aligned Process Supervision for Text-to-SQLcited by 0