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Grounded Adaptation for Zero-shot Executable Semantic Parsing

arXiv 2009.07396 ↗First posted 16 Sep 2020cs.CL cs.AI cs.DBcited by 76

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Abstract (arXiv)

We propose Grounded Adaptation for Zero-shot Executable Semantic Parsing (GAZP) to adapt an existing semantic parser to new environments (e.g. new database schemas). GAZP combines a forward semantic parser with a backward utterance generator to synthesize data (e.g. utterances and SQL queries) in the new environment, then selects cycle-consistent examples to adapt the parser. Unlike data-augmentation, which typically synthesizes unverified examples in the training environment, GAZP synthesizes examples in the new environment whose input-output consistency are verified. On the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks, GAZP improves logical form and execution accuracy of the baseline parser. Our analyses show that GAZP outperforms data-augmentation in the training environment, performance increases with the amount of GAZP-synthesized data, and cycle-consistency is central to successful adaptation.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Sep 2020.

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Citing papers per year: 2021: 19; 2022: 20; 2023: 18; 2024: 6; 2025: 10; 2026: 3.

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2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 667
2021A Survey of Data Augmentation Approaches for NLPcited by 408
2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
2023Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsingcited by 93
2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 90
2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
2021Break-It-Fix-It: Unsupervised Learning for Program Repaircited by 71
2021Natural SQL: Making SQL Easier to Infer from Natural Language Specificationscited by 66
2023ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thoughtcited by 65
2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47

38 more

2021Improving Compositional Generalization with Latent Structure and Data Augmentationcited by 46
2023Skill-Based Few-Shot Selection for In-Context Learningcited by 46
2021Grounding 'Grounding' in NLPcited by 44
2021Learning to Synthesize Data for Semantic Parsingcited by 41
2022SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Modelscited by 38
2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
2024Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLMcited by 35
2023Evaluating Cross-Domain Text-to-SQL Models and Benchmarkscited by 32
2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
2021Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answeringcited by 25
2023Interleaving Pre-Trained Language Models and Large Language Models for Zero-Shot NL2SQL Generationcited by 24
2023Selective Demonstrations for Cross-domain Text-to-SQLcited by 21
2021Zero-Shot Cross-lingual Semantic Parsingcited by 19
2022STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsingcited by 19
2023S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Modelscited by 18
2022PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generationcited by 16
2023Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrievalcited by 16
2021From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decodingcited by 14
2023From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuningcited by 14
2024CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editionscited by 13
2022Privacy-Preserving Domain Adaptation of Semantic Parserscited by 12
2023Generating Data for Symbolic Language with Large Language Modelscited by 12
2023Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chaincited by 12
2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11
2022CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parserscited by 11
2022Meta-Learning a Cross-lingual Manifold for Semantic Parsingcited by 11
2024You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQLcited by 11
2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
2021DG2: Data Augmentation Through Document Grounded Dialogue Generationcited by 10
2023LLM-SQL-Solver: Can LLMs Determine SQL Equivalence?cited by 10
2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
2021SPARQLing Database Queries from Intermediate Question Decompositionscited by 7
2022Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentationcited by 7
2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
2022Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parserscited by 7
2023Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsingcited by 6
2025Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotationcited by 6

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