Data to 7 Oct 2026

On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

arXiv 2010.11246 ↗First posted 21 Oct 2020cs.CL cs.AIcited by 59

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Abstract (arXiv)

Large-scale semantic parsing datasets annotated with logical forms have enabled major advances in supervised approaches. But can richer supervision help even more? To explore the utility of fine-grained, lexical-level supervision, we introduce Squall, a dataset that enriches 11,276 WikiTableQuestions English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. Our annotation enables new training possibilities for encoder-decoder models, including approaches from machine translation previously precluded by the absence of alignments. We propose and test two methods: (1) supervised attention; (2) adopting an auxiliary objective of disambiguating references in the input queries to table columns. In 5-fold cross validation, these strategies improve over strong baselines by 4.4% execution accuracy. Oracle experiments suggest that annotated alignments can support further accuracy gains of up to 23.9%.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2020.

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Citing papers per year: 2021: 4; 2022: 15; 2023: 12; 2024: 17; 2025: 9; 2026: 2.

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2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 228
2022Binding Language Models in Symbolic Languagescited by 224
2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
2021HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generationcited by 117
2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 54
2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
2023Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustnesscited by 50
2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
2022Reasoning Like Program Executorscited by 45
2022PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingcited by 41

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2023Open-WikiTable: Dataset for Open Domain Question Answering with Complex Reasoning over Tablecited by 40
2023MultiTabQA: Generating Tabular Answers for Multi-Table Question Answeringcited by 32
2024AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queriescited by 32
2022OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answeringcited by 29
2024A Survey of Table Reasoning with Large Language Modelscited by 29
2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
2022Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbationcited by 28
2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 27
2023TempTabQA: Temporal Question Answering for Semi-Structured Tablescited by 27
2024ProTrix: Building Models for Planning and Reasoning over Tables with Sentence Contextcited by 23
2023L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Modelscited by 22
2024Tree-of-Table: Unleashing the Power of LLMs for Enhanced Large-Scale Table Understandingcited by 20
2023S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Modelscited by 18
2024Interpretable LLM-based Table Question Answeringcited by 18
2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systemscited by 15
2023From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuningcited by 14
2023UNITE: A Unified Benchmark for Text-to-SQL Evaluationcited by 14
2025TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answeringcited by 14
2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12
2023Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Datacited by 11
2024BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domaincited by 10
2024SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQAcited by 9
2025Sparks of Tabular Reasoning via Text2SQL Reinforcement Learningcited by 9
2026Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Searchcited by 8
2022SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parserscited by 7
2025General Table Question Answering via Answer-Formula Joint Generationcited by 7
2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLcited by 6
2022Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Frameworkcited by 6
2022Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsingcited by 5
2023Benchmarking Diverse-Modal Entity Linking with Generative Modelscited by 5
2025STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsingcited by 5
2022Translate First Reorder Later: Leveraging Monotonicity in Semantic Parsingcited by 3
2022Leveraging Data Recasting to Enhance Tabular Reasoningcited by 3
2023InfoSync: Information Synchronization across Multilingual Semi-structured Tablescited by 3
2024Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansioncited by 3
2024Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Datacited by 3
2024Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answeringcited by 3
2024Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Modelscited by 2

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