  Most deep learning approaches for text-to-SQL generation are limited to the
WikiSQL dataset, which only supports very simple queries over a single table.
We focus on the Spider dataset, a complex and cross-domain text-to-SQL task,
which includes complex queries over multiple tables. In this paper, we propose
a SQL clause-wise decoding neural architecture with a self-attention based
database schema encoder to address the Spider task. Each of the clause-specific
decoders consists of a set of sub-modules, which is defined by the syntax of
each clause. Additionally, our model works recursively to support nested
queries. When evaluated on the Spider dataset, our approach achieves 4.6\% and
9.8\% accuracy gain in the test and dev sets, respectively. In addition, we
show that our model is significantly more effective at predicting complex and
nested queries than previous work.
