NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions

Fuxiang Chen, Seung-won Hwang, Jaegul Choo, Jung-Woo Ha, Sunghun Kim

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Abstract
Generating SQL codes from natural language questions (NL2SQL) is an emerging research area. Existing studies have mainly focused on clear scenarios where specified information is fully given to generate a SQL query. However, in developer forums such as Stack Overflow, questions cover more diverse tasks including table manipulation or performance issues, where a table is not specified. The SQL query posted in Stack Overflow, Pseudo-SQL (pSQL), does not usually contain table schemas and is not necessarily executable, is sufficient to guide developers. Here we describe a new NL2pSQL task to generate pSQL codes from natural language questions on under-specified database issues, NL2pSQL. In addition, we define two new metrics suitable for the proposed NL2pSQL task, Canonical-BLEU and SQL-BLEU, instead of the conventional BLEU. With a baseline model using sequence-to-sequence architecture integrated by denoising autoencoder, we confirm the validity of our task. Experiments show that the proposed NL2pSQL approach yields well-formed queries (up to 43% more than a standard Seq2Seq model). Our code and datasets will be publicly released.
Anthology ID:
D19-1262
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2603–2613
Language:
URL:
https://aclanthology.org/D19-1262
DOI:
10.18653/v1/D19-1262
Bibkey:
Cite (ACL):
Fuxiang Chen, Seung-won Hwang, Jaegul Choo, Jung-Woo Ha, and Sunghun Kim. 2019. NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions. 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 2603–2613, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
NL2pSQL: Generating Pseudo-SQL Queries from Under-Specified Natural Language Questions (Chen et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/teach-a-man-to-fish/D19-1262.pdf
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