Abstract
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 recentlyreleased 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%.