• Corpus ID: 173188416

Grammar-based Neural Text-to-SQL Generation

@article{Lin2019GrammarbasedNT,
  title={Grammar-based Neural Text-to-SQL Generation},
  author={Kevin Lin and Ben Bogin and Mark Neumann and Jonathan Berant and Matt Gardner},
  journal={ArXiv},
  year={2019},
  volume={abs/1905.13326}
}
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar. [] Key Result We analyze these techniques on ATIS and Spider, two challenging text-to-SQL datasets, demonstrating that they yield 14--18\% relative reductions in error.

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