  This paper investigates the effectiveness of pre-training for few-shot intent
classification. While existing paradigms commonly further pre-train language
models such as BERT on a vast amount of unlabeled corpus, we find it highly
effective and efficient to simply fine-tune BERT with a small set of labeled
utterances from public datasets. Specifically, fine-tuning BERT with roughly
1,000 labeled data yields a pre-trained model -- IntentBERT, which can easily
surpass the performance of existing pre-trained models for few-shot intent
classification on novel domains with very different semantics. The high
effectiveness of IntentBERT confirms the feasibility and practicality of
few-shot intent detection, and its high generalization ability across different
domains suggests that intent classification tasks may share a similar
underlying structure, which can be efficiently learned from a small set of
labeled data. The source code can be found at
https://github.com/hdzhang-code/IntentBERT.
