Structure-Grounded Pretraining for Text-to-SQL

Understand

Learning to capture text-table alignment is essential for tasks like text-to-SQL.

• A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema.
• In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (StruG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus.
• We identify a set of novel prediction tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder.