  Practical dialogue systems require robust methods of detecting out-of-scope
(OOS) utterances to avoid conversational breakdowns and related failure modes.
Directly training a model with labeled OOS examples yields reasonable
performance, but obtaining such data is a resource-intensive process. To tackle
this limited-data problem, previous methods focus on better modeling the
distribution of in-scope (INS) examples. We introduce GOLD as an orthogonal
technique that augments existing data to train better OOS detectors operating
in low-data regimes. GOLD generates pseudo-labeled candidates using samples
from an auxiliary dataset and keeps only the most beneficial candidates for
training through a novel filtering mechanism. In experiments across three
target benchmarks, the top GOLD model outperforms all existing methods on all
key metrics, achieving relative gains of 52.4%, 48.9% and 50.3% against median
baseline performance. We also analyze the unique properties of OOS data to
identify key factors for optimally applying our proposed method.
