mlsanityData to 7 Oct 2026

Controllable Data Augmentation for Context-Dependent Text-to-SQL

arXiv 2304.13902 ↗First posted 27 Apr 2023cs.CLcited by 1

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Abstract (arXiv)

The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used method to solve this problem. However, the data generated by current augmentation methods often lack diversity. In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results. We designed the SQL dialogue state to enhance the data diversity through the state transition. Meanwhile, we also present a filter method to ensure the data quality by a grounding model. Additionally, we utilize a grounding model to identify and filter low-quality questions that mismatch the state information. Experimental results on the SParC and CoSQL datasets show that ConDA boosts the baseline model to achieve an average improvement of 3.3% on complex questions. Moreover, we analyze the augmented data, which reveals that the data generated by ConDA are of high quality in both SQL template hardness and types, turns, and question consistency.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

30 of 41 references.

References · 30

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  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
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  5. 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 760
  6. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  7. 2019CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databasescited by 180
  8. 2020GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsingcited by 171
  9. 2019SParC: Cross-Domain Semantic Parsing in Contextcited by 141
  10. 2021LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relationscited by 119
  11. 2016Data Recombination for Neural Semantic Parsingcited by 111
  12. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
18 more
  1. 2019Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questionscited by 80
  2. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  3. 2018SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Taskcited by 74
  4. 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 67
  5. 2021A Survey on Spoken Language Understanding: Recent Advances and New Frontierscited by 53
  6. 2020CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackerscited by 43
  7. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  8. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  9. 2018Question Generation from SQL Queries Improves Neural Semantic Parsingcited by 29
  10. 2020IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generationcited by 29
  11. 2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
  12. 2019An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answeringcited by 22
  13. 2022UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQLcited by 20
  14. 2020Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsingcited by 18
  15. 2021Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQLcited by 13
  16. 2022CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parserscited by 11
  17. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
  18. 2022Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentationcited by 7

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Citing papers per year: 2024: 1.

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  1. 2024QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQLcited by 5