  Single-table text-to-SQL aims to transform a natural language question into a
SQL query according to one single table. Recent work has made promising
progress on this task by pre-trained language models and a multi-submodule
framework. However, zero-shot table, that is, the invisible table in the
training set, is currently the most critical bottleneck restricting the
application of existing approaches to real-world scenarios. Although some work
has utilized auxiliary tasks to help handle zero-shot tables, expensive extra
manual annotation limits their practicality. In this paper, we propose a new
approach for the zero-shot text-to-SQL task which does not rely on any
additional manual annotations. Our approach consists of two parts. First, we
propose a new model that leverages the abundant information of table content to
help establish the mapping between questions and zero-shot tables. Further, we
propose a simple but efficient meta-learning strategy to train our model. The
strategy utilizes the two-step gradient update to force the model to learn a
generalization ability towards zero-shot tables. We conduct extensive
experiments on a public open-domain text-to-SQL dataset WikiSQL and a
domain-specific dataset ESQL. Compared to existing approaches using the same
pre-trained model, our approach achieves significant improvements on both
datasets. Compared to the larger pre-trained model and the tabular-specific
pre-trained model, our approach is still competitive. More importantly, on the
zero-shot subsets of both the datasets, our approach further increases the
improvements.
