SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task

TL;DR

SyntaxSQLNet introduces a syntax tree-based neural network for complex, cross-domain text-to-SQL tasks, significantly improving accuracy on the challenging Spider benchmark by leveraging syntax-aware decoding and data augmentation.

Contribution

The paper presents the first approach specifically designed for complex, cross-domain text-to-SQL generation, utilizing syntax tree decoding and cross-domain augmentation techniques.

Findings

Outperforms previous models by 7.3% in exact match accuracy.

Achieves an additional 7.5% improvement through data augmentation.

Handles more complex SQL queries with multiple clauses and nested subqueries.

Abstract

Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on the Spider text-to-SQL task, which contains databases with multiple tables and complex SQL queries with multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 7.3% in exact matching accuracy. We also show that SyntaxSQLNet can further improve the performance by an additional 7.5% using a cross-domain augmentation method, resulting in a 14.8% improvement in total. To our knowledge, we are the first to study this complex and cross-domain text-to-SQL task.