"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
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Abstract (arXiv)
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users' natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 1; 2025: 0; 2026: 1.
11 papers.
- 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
- 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expectcited by 47
- 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
- 2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsingcited by 28
- 2023Text-to-SQL Error Correction with Language Models of Codecited by 21
- 2023Interactive Text-to-SQL Generation via Editable Step-by-Step Explanationscited by 19
- 2023Learning to Simulate Natural Language Feedback for Interactive Semantic Parsingcited by 19
- 2023Error Detection for Text-to-SQL Semantic Parsingcited by 11
- 2024SQLucid: Grounding Natural Language Database Queries with Interactive Explanationscited by 10
- 2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQLcited by 6
- 2026CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queriescited by 0
- 2026CLARA: Clarification of Language Ambiguity through Result Analysis for Natural-Language Cancer Genomics Queries3 Aug 2026
- 2024SQLucid: Grounding Natural Language Database Queries with Interactive Explanations10 Sep 2024
- 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQL23 May 2023
- 2023Error Detection for Text-to-SQL Semantic Parsing23 May 2023
- 2023Text-to-SQL Error Correction with Language Models of Code22 May 2023
- 2023Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing14 May 2023
- 2023Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations12 May 2023
- 2022Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL17 Dec 2022
- 2022Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect22 Aug 2022
- 2021Awakening Latent Grounding from Pretrained Language Models for Semantic Parsing22 Sep 2021
- 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser10 Apr 2021