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

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.

arXiv commentEMNLP 2018, Long Paper

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Cited by in corpus (6)

• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
• Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
• Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
• DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks
• Generating Logical Forms from Graph Representations of Text and Entities
• Self-supervised Text-to-SQL Learning with Header Alignment Training