mlsanityData to 7 Oct 2026

CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers

arXiv 2205.07686 ↗First posted 16 May 2022cs.CLcited by 11

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Abstract (arXiv)

Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 33 of its 45 references that are in the map. First posted on arXiv 16 May 2022.

Builds on

33 of 45 references.

References · 33

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  8. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  9. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  10. 2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 402
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21 more
  1. 2020ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-trainingcited by 221
  2. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 180
  3. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  4. 2016Improving the Robustness of Deep Neural Networks via Stability Trainingcited by 156
  5. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  6. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  7. 2021COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretrainingcited by 126
  8. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  9. 2022RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQLcited by 85
  10. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  11. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  12. 2020How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Contextcited by 53
  13. 2021Consistency Regularization for Cross-Lingual Fine-Tuningcited by 41
  14. 2020IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generationcited by 29
  15. 2021Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answeringcited by 27
  16. 2022HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsingcited by 26
  17. 2018Learning to Map Context-Dependent Sentences to Executable Formal Queriescited by 23
  18. 2020Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsingcited by 18
  19. 2021Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQLcited by 13
  20. 2021Memory-Based Semantic Parsingcited by 6
  21. 2021Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQLcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 0; 2026: 1.

11 papers.

  1. 2023A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capabilitycited by 118
  2. 2024StructLM: Towards Building Generalist Models for Structured Knowledge Groundingcited by 29
  3. 2024CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editionscited by 13
  4. 2022Q-TOD: A Query-driven Task-oriented Dialogue Systemcited by 10
  5. 2022MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQLcited by 5
  6. 2024EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Recordscited by 5
  7. 2023StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structurecited by 2
  8. 2023Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsingcited by 2
  9. 2023Controllable Data Augmentation for Context-Dependent Text-to-SQLcited by 1
  10. 2024LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarioscited by 0
  11. 2026Track-SQL: Enhancing Generative Language Models with Dual-Extractive Modules for Schema and Context Tracking in Multi-turn Text-to-SQLcited by 0