  We introduce a novel setup for low-resource task-oriented semantic parsing
which incorporates several constraints that may arise in real-world scenarios:
(1) lack of similar datasets/models from a related domain, (2) inability to
sample useful logical forms directly from a grammar, and (3) privacy
requirements for unlabeled natural utterances. Our goal is to improve a
low-resource semantic parser using utterances collected through user
interactions. In this highly challenging but realistic setting, we investigate
data augmentation approaches involving generating a set of structured canonical
utterances corresponding to logical forms, before simulating corresponding
natural language and filtering the resulting pairs. We find that such
approaches are effective despite our restrictive setup: in a low-resource
setting on the complex SMCalFlow calendaring dataset (Andreas et al., 2020), we
observe 33% relative improvement over a non-data-augmented baseline in top-1
match.
