One-Shot Learning for Text-to-SQL Generation
This addresses a limitation in natural language interfaces to databases for users needing complex queries, but it is incremental as it builds on template-based approaches.
The paper tackles the problem of text-to-SQL generation for complex queries, where existing methods fail to handle unseen SQL templates, and proposes a one-shot learning model that improves SQL generation accuracy for trained templates and adapts to unseen templates based on a single example without additional training.
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.