  Most existing studies in text-to-SQL tasks do not require generating complex
SQL queries with multiple clauses or sub-queries, and generalizing to new,
unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network
to address the complex and cross-domain text-to-SQL generation task.
SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL
generation path history and table-aware column attention encoders. We evaluate
SyntaxSQLNet on the Spider text-to-SQL task, which contains databases with
multiple tables and complex SQL queries with multiple SQL clauses and nested
queries. We use a database split setting where databases in the test set are
unseen during training. Experimental results show that SyntaxSQLNet can handle
a significantly greater number of complex SQL examples than prior work,
outperforming the previous state-of-the-art model by 7.3% in exact matching
accuracy. We also show that SyntaxSQLNet can further improve the performance by
an additional 7.5% using a cross-domain augmentation method, resulting in a
14.8% improvement in total. To our knowledge, we are the first to study this
complex and cross-domain text-to-SQL task.
