# One-Shot Learning for Text-to-SQL Generation

arXiv: https://arxiv.org/abs/1905.11499 · HTML: https://mlsanity.com/paper/1905.11499/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization](https://mlsanity.com/paper/2109.02575/) (2021)
- [Meta-Learning a Cross-lingual Manifold for Semantic Parsing](https://mlsanity.com/paper/2209.12577/) (2022)
- [Few-Shot Semantic Parsing for New Predicates](https://mlsanity.com/paper/2101.10708/) (2021)
- [Compositional Generalization for Multi-label Text Classification: A Data-Augmentation Approach](https://mlsanity.com/paper/2312.11276/) (2023)
- [Semantic Parsing in Limited Resource Conditions](https://mlsanity.com/paper/2309.07429/) (2023)

## Abstract (arXiv)

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.