  In this work, we focus on a more challenging few-shot intent detection
scenario where many intents are fine-grained and semantically similar. We
present a simple yet effective few-shot intent detection schema via contrastive
pre-training and fine-tuning. Specifically, we first conduct self-supervised
contrastive pre-training on collected intent datasets, which implicitly learns
to discriminate semantically similar utterances without using any labels. We
then perform few-shot intent detection together with supervised contrastive
learning, which explicitly pulls utterances from the same intent closer and
pushes utterances across different intents farther. Experimental results show
that our proposed method achieves state-of-the-art performance on three
challenging intent detection datasets under 5-shot and 10-shot settings.
