Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning

• URL: http://arxiv.org/abs/2109.05395v1
• Date: Sun, 12 Sep 2021 01:01:28 GMT
• Title: Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
• Authors: Yongrui Chen, Xinnan Guo, Chaojie Wang, Jian Qiu, Guilin Qi, Meng Wang, Huiying Li
• Abstract summary: Single-table text-to-one aims to transform a natural language question into a query according to one single table. We propose a new approach for the zero-shot text-to-one task which does not rely on any additional manual annotations. We conduct extensive experiments on a public open-domain text-to-one dataset and a domain-specific dataset E.
• Score: 25.69875174742935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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