  Most deep learning approaches for text-to-SQL generation are limited to the
WikiSQL dataset, which only supports very simple queries. Recently,
template-based and sequence-to-sequence approaches were proposed to support
complex queries, which contain join queries, nested queries, and other types.
However, Finegan-Dollak et al. (2018) demonstrated that both the approaches
lack the ability to generate SQL of unseen templates. In this paper, we propose
a template-based one-shot learning model for the text-to-SQL generation so that
the model can generate SQL of an untrained template based on a single example.
First, we classify the SQL template using the Matching Network that is
augmented by our novel architecture Candidate Search Network. Then, we fill the
variable slots in the predicted template using the Pointer Network. We show
that our model outperforms state-of-the-art approaches for various text-to-SQL
datasets in two aspects: 1) the SQL generation accuracy for the trained
templates, and 2) the adaptability to the unseen SQL templates based on a
single example without any additional training.
