Global Reasoning over Database Structures for Text-to-SQL Parsing

Understand

State-of-the-art semantic parsers rely on auto-regressive decoding, emitting one symbol at a time.

• When tested against complex databases that are unobserved at training time (zero-shot), the parser often struggles to select the correct set of database constants in the new database, due to the local nature of decoding.
• In this work, we propose a semantic parser that globally reasons about the structure of the output query to make a more contextually-informed selection of database constants.
• We use message-passing through a graph neural network to softly select a subset of database constants for the output query, conditioned on the question.