  Most prior work on task-oriented dialogue systems is restricted to supporting
domain APIs. However, users may have requests that are out of the scope of
these APIs. This work focuses on identifying such user requests. Existing
methods for this task mainly rely on fine-tuning pre-trained models on large
annotated data. We propose a novel method, REDE, based on adaptive
representation learning and density estimation. REDE can be applied to
zero-shot cases, and quickly learns a high-performing detector with only a few
shots by updating less than 3K parameters. We demonstrate REDE's competitive
performance on DSTC9 data and our newly collected test set.
