plied a sequence-to-sequence approach with attention mechanism, and<cite> Finegan-Dollak et al. (2018)</cite> proposed a template-based model and another sequence-to-sequence model with a copy mechanism. However,<cite> Finegan-Dollak et al. (2018)</cite> showed that both approaches lack the ability to generate SQL of the unseen template in the training stage. One-shot Learning/Matching Network Deep learning models usually require hundreds or thousands of examples in order to learn a class. To overcome this limitation, one-shot learning aims to learn a class from a single labeled example. We applied one-shot learning to the text-to-SQL task so that our model could learn a SQL template from just a few examples and adapt easily and promptly to the SQL of untrained templates.