  Progress in natural language interfaces to databases (NLIDB) has been slow
mainly due to linguistic issues (such as language ambiguity) and domain
portability. Moreover, the lack of a large corpus to be used as a standard
benchmark has made data-driven approaches difficult to develop and compare. In
this paper, we revisit the problem of NLIDBs and recast it as a sequence
translation problem. To this end, we introduce a large dataset extracted from
the Stack Exchange Data Explorer website, which can be used for training neural
natural language interfaces for databases. We also report encouraging baseline
results on a smaller manually annotated test corpus, obtained using an
attention-based sequence-to-sequence neural network.
