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        <title>Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing</title>
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    <abstract>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 network, and this representation is later used at both encoding and decoding time. 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%.</abstract>
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%0 Conference Proceedings
%T Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
%A Bogin, Ben
%A Berant, Jonathan
%A Gardner, Matt
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bogin-etal-2019-representing
%X 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 network, and this representation is later used at both encoding and decoding time. 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%.
%R 10.18653/v1/P19-1448
%U https://aclanthology.org/P19-1448
%U https://doi.org/10.18653/v1/P19-1448
%P 4560-4565

Markdown (Informal)

[Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://aclanthology.org/P19-1448) (Bogin et al., ACL 2019)

• Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (Bogin et al., ACL 2019)

ACL

• Ben Bogin, Jonathan Berant, and Matt Gardner. 2019. Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4560–4565, Florence, Italy. Association for Computational Linguistics.