  Intent detection is one of the core components of goal-oriented dialog
systems, and detecting out-of-scope (OOS) intents is also a practically
important skill. Few-shot learning is attracting much attention to mitigate
data scarcity, but OOS detection becomes even more challenging. In this paper,
we present a simple yet effective approach, discriminative nearest neighbor
classification with deep self-attention. Unlike softmax classifiers, we
leverage BERT-style pairwise encoding to train a binary classifier that
estimates the best matched training example for a user input. We propose to
boost the discriminative ability by transferring a natural language inference
(NLI) model. Our extensive experiments on a large-scale multi-domain intent
detection task show that our method achieves more stable and accurate in-domain
and OOS detection accuracy than RoBERTa-based classifiers and embedding-based
nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot
model to perform competitively with 50-shot or even full-shot classifiers,
while we can keep the inference time constant by leveraging a faster embedding
retrieval model.
