  Detecting Out-of-Domain (OOD) or unknown intents from user queries is
essential in a task-oriented dialog system. A key challenge of OOD detection is
to learn discriminative semantic features. Traditional cross-entropy loss only
focuses on whether a sample is correctly classified, and does not explicitly
distinguish the margins between categories. In this paper, we propose a
supervised contrastive learning objective to minimize intra-class variance by
pulling together in-domain intents belonging to the same class and maximize
inter-class variance by pushing apart samples from different classes. Besides,
we employ an adversarial augmentation mechanism to obtain pseudo diverse views
of a sample in the latent space. Experiments on two public datasets prove the
effectiveness of our method capturing discriminative representations for OOD
detection.
