GAN-Based Out-of-Domain Detection Using Both In-Domain and Out-of-Domain Samples

In domain classification for spoken language understanding, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs between users and the systems. In the situation where both in-domain (ID) and OOD samples are available, our goal is to take advantage of OOD samples under the GAN-based framework for OOD detection. We propose a GAN-based OOD detector with OOD prior distribution and weighted loss (WOODP-GAN). The model consists of a GAN-based detector with OOD prior distribution for generating effective pseudo OOD samples, and a weighted loss function for balancing the loss of fake OOD samples against real OOD samples in the discriminator. Extensive experiments show our proposed WOODP-GAN model outperforms the existing methods in the benchmark dataset CLINC150.