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

Abstract

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.

arXiv commentACL 2021 demonstration track

References in corpus (5)

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• Improving Text-to-SQL Evaluation Methodology
• Content Enhanced BERT-based Text-to-SQL Generation
• TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
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