  A common recent approach to semantic parsing augments sequence-to-sequence
models by retrieving and appending a set of training samples, called exemplars.
The effectiveness of this recipe is limited by the ability to retrieve
informative exemplars that help produce the correct parse, which is especially
challenging in low-resource settings. Existing retrieval is commonly based on
similarity of query and exemplar inputs. We propose GandR, a retrieval
procedure that retrieves exemplars for which outputs are also similar.
GandRfirst generates a preliminary prediction with input-based retrieval. Then,
it retrieves exemplars with outputs similar to the preliminary prediction which
are used to generate a final prediction. GandR sets the state of the art on
multiple low-resource semantic parsing tasks.
