  Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog
system. Most dialog systems are trained on a pool of annotated OOD data to
achieve this goal. However, collecting the annotated OOD data for a given
domain is an expensive process. To mitigate this issue, previous works have
proposed generative adversarial networks (GAN) based models to generate OOD
data for a given domain automatically. However, these proposed models do not
work directly with the text. They work with the text's latent space instead,
enforcing these models to include components responsible for encoding text into
latent space and decoding it back, such as auto-encoder. These components
increase the model complexity, making it difficult to train. We propose OodGAN,
a sequential generative adversarial network (SeqGAN) based model for OOD data
generation. Our proposed model works directly on the text and hence eliminates
the need to include an auto-encoder. OOD data generated using OodGAN model
outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative
improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR
0.95) (Zheng et al., 2020).
