  The importance of building semantic parsers which can be applied to new
domains and generate programs unseen at training has long been acknowledged,
and datasets testing out-of-domain performance are becoming increasingly
available. However, little or no attention has been devoted to learning
algorithms or objectives which promote domain generalization, with virtually
all existing approaches relying on standard supervised learning. In this work,
we use a meta-learning framework which targets zero-shot domain generalization
for semantic parsing. We apply a model-agnostic training algorithm that
simulates zero-shot parsing by constructing virtual train and test sets from
disjoint domains. The learning objective capitalizes on the intuition that
gradient steps that improve source-domain performance should also improve
target-domain performance, thus encouraging a parser to generalize to unseen
target domains. Experimental results on the (English) Spider and Chinese Spider
datasets show that the meta-learning objective significantly boosts the
performance of a baseline parser.
