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

• URL: http://arxiv.org/abs/2106.04559v1
• Date: Tue, 8 Jun 2021 17:46:20 GMT
• Title: Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface
• Authors: Peng Xu, Wenjie Zi, Hamidreza Shahidi, \'Akos K\'ad\'ar, Keyi Tang, Wei Yang, Jawad Ateeq, Harsh Barot, Meidan Alon, Yanshuai Cao
• Abstract summary: Natural language database interface (NLDB) can democratize data-driven insights for non-technical users. This work presents Turing, a NLDB system toward bridging this gap. The cross-domain semantic validation method of Turing achieves $751%$ execution accuracy, and $78.3%$ top-5 beam execution accuracy on the Spider set.
• Score: 11.782395912109324
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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