  Research on parsing language to SQL has largely ignored the structure of the
database (DB) schema, either because the DB was very simple, or because it was
observed at both training and test time. In Spider, a recently-released
text-to-SQL dataset, new and complex DBs are given at test time, and so the
structure of the DB schema can inform the predicted SQL query. In this paper,
we present an encoder-decoder semantic parser, where the structure of the DB
schema is encoded with a graph neural network, and this representation is later
used at both encoding and decoding time. Evaluation shows that encoding the
schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically
above the current state of the art, which is at 19.7%.
