  We present a simple way to do the task of text-to-SQL problem with weak
supervision. We call it Rule-SQL. Given the question and the answer from the
database table without the SQL logic form, Rule-SQL use the rules based on
table column names and question string for the SQL exploration first and then
use the explored SQL for supervised training. We design several rules for
reducing the exploration search space. For the deep model, we leverage BERT for
the representation layer and separate the model to SELECT, AGG and WHERE parts.
The experiment result on WikiSQL outperforms the strong baseline of full
supervision and is comparable to the start-of-the-art weak supervised mothods.
