  Generating structural query language (SQL) queries from natural language is a
long-standing open problem. Answering a natural language question about a
database table requires modeling complex interactions between the columns of
the table and the question. In this paper, we apply the synthesizing approach
to solve this problem. Based on the structure of SQL queries, we break down the
model to three sub-modules and design specific deep neural networks for each of
them. Taking inspiration from the similar machine reading task, we employ the
bidirectional attention mechanisms and character-level embedding with
convolutional neural networks (CNNs) to improve the result. Experimental
evaluations show that our model achieves the state-of-the-art results in
WikiSQL dataset.
