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Progress in natural language interfaces to databases (NLIDB) has been slowmainly due to linguistic issues (such as language ambiguity) and domainportability. Moreover, the lack of a large corpus to be used as a standardbenchmark has made data-driven approaches difficult to develop and compare. Inthis paper, we revisit the problem of NLIDBs and recast it as a sequencetranslation problem. To this end, we introduce a large dataset extracted fromthe Stack Exchange Data Explorer website, which can be used for training neuralnatural language interfaces for databases. We also report encouraging baselineresults on a smaller manually annotated test corpus, obtained using anattention-based sequence-to-sequence neural network.

Dataset for a Neural Natural Language Interface for Databases (NNLIDB)