End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task

Abstract

In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better encoding ability, we propose a column selection auxiliary task to empower the encoder with the relevance matching capability by using explicit learning targets. Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL. With experiments on Spider, our proposed framework improves over the baselines on the execution accuracy and exact set match accuracy when database contents are unavailable, and detailed analysis sheds light on future work.

References in corpus (4)

• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
• Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing
• Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
• Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing