Robust Text-to-SQL Generation with Execution-Guided Decoding

Understand

We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.

• We introduce a new mechanism, execution guidance, to leverage the semantics of SQL.
• It detects and excludes faulty programs during the decoding procedure by conditioning on the execution of partially generated program.
• The mechanism can be used with any autoregressive generative model, which we demonstrate on four state-of-the-art recurrent or template-based semantic parsing models.