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        <title>Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation</title>
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        <namePart type="given">Dongjun</namePart>
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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        <name type="personal">
            <namePart type="given">Jing</namePart>
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    <abstract>Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table. We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables. In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task. Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause. Additionally, our model works recursively to support nested queries. When evaluated on the Spider dataset, our approach achieves 4.6% and 9.8% accuracy gain in the test and dev sets, respectively. In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1624</identifier>
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%0 Conference Proceedings
%T Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation
%A Lee, Dongjun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lee-2019-clause
%X Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table. We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables. In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task. Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause. Additionally, our model works recursively to support nested queries. When evaluated on the Spider dataset, our approach achieves 4.6% and 9.8% accuracy gain in the test and dev sets, respectively. In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.
%R 10.18653/v1/D19-1624
%U https://aclanthology.org/D19-1624
%U https://doi.org/10.18653/v1/D19-1624
%P 6045-6051

Markdown (Informal)

[Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation](https://aclanthology.org/D19-1624) (Lee, EMNLP-IJCNLP 2019)

• Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation (Lee, EMNLP-IJCNLP 2019)

ACL

• Dongjun Lee. 2019. Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6045–6051, Hong Kong, China. Association for Computational Linguistics.