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    <abstract>Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on a new large-scale text-to-SQL corpus containing databases with multiple tables and complex SQL queries containing multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5% in exact matching accuracy. To our knowledge, we are the first to study this complex text-to-SQL task. Our task and models with the latest updates are available at https://yale-lily.github.io/seq2sql/spider.</abstract>
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%0 Conference Proceedings
%T SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
%A Yu, Tao
%A Yasunaga, Michihiro
%A Yang, Kai
%A Zhang, Rui
%A Wang, Dongxu
%A Li, Zifan
%A Radev, Dragomir
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F yu-etal-2018-syntaxsqlnet
%X Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on a new large-scale text-to-SQL corpus containing databases with multiple tables and complex SQL queries containing multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5% in exact matching accuracy. To our knowledge, we are the first to study this complex text-to-SQL task. Our task and models with the latest updates are available at https://yale-lily.github.io/seq2sql/spider.
%R 10.18653/v1/D18-1193
%U https://aclanthology.org/D18-1193
%U https://doi.org/10.18653/v1/D18-1193
%P 1653-1663

Markdown (Informal)

[SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task](https://aclanthology.org/D18-1193) (Yu et al., EMNLP 2018)

• SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (Yu et al., EMNLP 2018)

ACL

• Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, and Dragomir Radev. 2018. SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1653–1663, Brussels, Belgium. Association for Computational Linguistics.