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Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface

arXiv 2106.04559 ↗First posted 8 Jun 2021cs.CLcited by 1

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Abstract (arXiv)

A natural language database interface (NLDB) can democratize data-driven insights for non-technical users. However, existing Text-to-SQL semantic parsers cannot achieve high enough accuracy in the cross-database setting to allow good usability in practice. This work presents Turing, a NLDB system toward bridging this gap. The cross-domain semantic parser of Turing with our novel value prediction method achieves 75.1% execution accuracy, and 78.3% top-5 beam execution accuracy on the Spider validation set. To benefit from the higher beam accuracy, we design an interactive system where the SQL hypotheses in the beam are explained step-by-step in natural language, with their differences highlighted. The user can then compare and judge the hypotheses to select which one reflects their intention if any. The English explanations of SQL queries in Turing are produced by our high-precision natural language generation system based on synchronous grammars.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 8 Jun 2021.

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17 of 24 references.

References · 17

  1. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  2. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,120
  3. 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
  4. 2019RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parserscited by 420
  5. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  6. 2020Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?cited by 147
  7. 2018Improving Text-to-SQL Evaluation Methodologycited by 133
  8. 2020Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsingcited by 133
  9. 2020SmBoP: Semi-autoregressive Bottom-up Semantic Parsingcited by 97
  10. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  11. 2020Grounded Adaptation for Zero-shot Executable Semantic Parsingcited by 76
  12. 2020RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databasescited by 63
5 more
  1. 2020Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedbackcited by 44
  2. 2020Optimizing Deeper Transformers on Small Datasetscited by 41
  3. 2018SQL-to-Text Generation with Graph-to-Sequence Modelcited by 31
  4. 2020Photon: A Robust Cross-Domain Text-to-SQL Systemcited by 26
  5. 2019Content Enhanced BERT-based Text-to-SQL Generationcited by 16

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Citing papers per year: 2021: 1.

1 paper.

  1. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11