@InProceedings{sun-EtAl:2018:Long,
  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},
  title     = {Semantic Parsing with Syntax- and Table-Aware SQL Generation},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  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%.},
  url       = {http://www.aclweb.org/anthology/P18-1034}
}