  Localizing a semantic parser to support new languages requires effective
cross-lingual generalization. Recent work has found success with
machine-translation or zero-shot methods although these approaches can struggle
to model how native speakers ask questions. We consider how to effectively
leverage minimal annotated examples in new languages for few-shot cross-lingual
semantic parsing. We introduce a first-order meta-learning algorithm to train a
semantic parser with maximal sample efficiency during cross-lingual transfer.
Our algorithm uses high-resource languages to train the parser and
simultaneously optimizes for cross-lingual generalization for lower-resource
languages. Results across six languages on ATIS demonstrate that our
combination of generalization steps yields accurate semantic parsers sampling
$\le$10% of source training data in each new language. Our approach also trains
a competitive model on Spider using English with generalization to Chinese
similarly sampling $\le$10% of training data.
