Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

@inproceedings{Bogin2019RepresentingSS,
  title={Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing},
  author={Ben Bogin and Jonathan Berant and Matthew Ph Gardner},
  booktitle={ACL},
  year={2019}
}
Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In \spider{}, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural… CONTINUE READING
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Key Quantitative Results

  • Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.None Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4560–4565 Florence, Italy, July 28 - August 2, 2019.
  • We evaluate our parser on SPIDER, and show that encoding the schema structure improves accuracy from 33.8% to 39.4% (and from 14.6% to 26.8% on questions that involve multiple tables), well beyond 19.7%, the current stateof-the-art.

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