  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.
