  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. Moreover, we train a model to rank queries based
on the global alignment of database constants to question words. We apply our
techniques to the current state-of-the-art model for Spider, a zero-shot
semantic parsing dataset with complex databases, increasing accuracy from 39.4%
to 47.4%.
