2018

Semantic Parsing with Syntax- and Table-Aware SQL Generation

Sun, Yibo, Tang, Duyu, Duan, Nan et al.

Understand

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 are 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.

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