  Unknown intent detection aims to identify the out-of-distribution (OOD)
utterance whose intent has never appeared in the training set. In this paper,
we propose using energy scores for this task as the energy score is
theoretically aligned with the density of the input and can be derived from any
classifier. However, high-quality OOD utterances are required during the
training stage in order to shape the energy gap between OOD and in-distribution
(IND), and these utterances are difficult to collect in practice. To tackle
this problem, we propose a data manipulation framework to Generate high-quality
OOD utterances with importance weighTs (GOT). Experimental results show that
the energy-based detector fine-tuned by GOT can achieve state-of-the-art
results on two benchmark datasets.
