@inproceedings{sun-etal-2018-semantic,
title = "Semantic Parsing with Syntax- and Table-Aware {SQL} Generation",
author = "Sun, Yibo and
Tang, Duyu and
Duan, Nan and
Ji, Jianshu and
Cao, Guihong and
Feng, Xiaocheng and
Qin, Bing and
Liu, Ting and
Zhou, Ming",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1034",
doi = "10.18653/v1/P18-1034",
pages = "361--372",
abstract = "We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question- SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0{\%} to 74.4{\%}.",
}
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%0 Conference Proceedings
%T Semantic Parsing with Syntax- and Table-Aware SQL Generation
%A Sun, Yibo
%A Tang, Duyu
%A Duan, Nan
%A Ji, Jianshu
%A Cao, Guihong
%A Feng, Xiaocheng
%A Qin, Bing
%A Liu, Ting
%A Zhou, Ming
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sun-etal-2018-semantic
%X We present a generative model to map natural language questions into SQL queries. Existing neural network based approaches typically generate a SQL query word-by-word, however, a large portion of the generated results is incorrect or not executable due to the mismatch between question words and table contents. Our approach addresses this problem by considering the structure of table and the syntax of SQL language. The quality of the generated SQL query is significantly improved through (1) learning to replicate content from column names, cells or SQL keywords; and (2) improving the generation of WHERE clause by leveraging the column-cell relation. Experiments are conducted on WikiSQL, a recently released dataset with the largest question- SQL pairs. Our approach significantly improves the state-of-the-art execution accuracy from 69.0% to 74.4%.
%R 10.18653/v1/P18-1034
%U https://aclanthology.org/P18-1034
%U https://doi.org/10.18653/v1/P18-1034
%P 361-372
Markdown (Informal)
[Semantic Parsing with Syntax- and Table-Aware SQL Generation](https://aclanthology.org/P18-1034) (Sun et al., ACL 2018)
ACL
- Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, and Ming Zhou. 2018. Semantic Parsing with Syntax- and Table-Aware SQL Generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 361–372, Melbourne, Australia. Association for Computational Linguistics.