  Natural language interfaces to databases (NLIDB) democratize end user access
to relational data. Due to fundamental differences between natural language
communication and programming, it is common for end users to issue questions
that are ambiguous to the system or fall outside the semantic scope of its
underlying query language. We present Photon, a robust, modular, cross-domain
NLIDB that can flag natural language input to which a SQL mapping cannot be
immediately determined. Photon consists of a strong neural semantic parser
(63.2\% structure accuracy on the Spider dev benchmark), a human-in-the-loop
question corrector, a SQL executor and a response generator. The question
corrector is a discriminative neural sequence editor which detects confusion
span(s) in the input question and suggests rephrasing until a translatable
input is given by the user or a maximum number of iterations are conducted.
Experiments on simulated data show that the proposed method effectively
improves the robustness of text-to-SQL system against untranslatable user
input. The live demo of our system is available at http://naturalsql.com.
