  In this paper, we formulate a more realistic and difficult problem setup for
the intent detection task in natural language understanding, namely Generalized
Few-Shot Intent Detection (GFSID). GFSID aims to discriminate a joint label
space consisting of both existing intents which have enough labeled data and
novel intents which only have a few examples for each class. To approach this
problem, we propose a novel model, Conditional Text Generation with BERT
(CG-BERT). CG-BERT effectively leverages a large pre-trained language model to
generate text conditioned on the intent label. By modeling the utterance
distribution with variational inference, CG-BERT can generate diverse
utterances for the novel intents even with only a few utterances available.
Experimental results show that CG-BERT achieves state-of-the-art performance on
the GFSID task with 1-shot and 5-shot settings on two real-world datasets.
