  Natural Language Understanding (NLU) is a vital component of dialogue
systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in
practical applications, since the acceptance of the OOD input that is
unsupported by the current system may lead to catastrophic failure. However,
most existing OOD detection methods rely heavily on manually labeled OOD
samples and cannot take full advantage of unlabeled data. This limits the
feasibility of these models in practical applications.
  In this paper, we propose a novel model to generate high-quality pseudo OOD
samples that are akin to IN-Domain (IND) input utterances, and thereby improves
the performance of OOD detection. To this end, an autoencoder is trained to map
an input utterance into a latent code. and the codes of IND and OOD samples are
trained to be indistinguishable by utilizing a generative adversarial network.
To provide more supervision signals, an auxiliary classifier is introduced to
regularize the generated OOD samples to have indistinguishable intent labels.
Experiments show that these pseudo OOD samples generated by our model can be
used to effectively improve OOD detection in NLU. Besides, we also demonstrate
that the effectiveness of these pseudo OOD data can be further improved by
efficiently utilizing unlabeled data.
