  Intent Detection is one of the tasks of the Natural Language Understanding
(NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of
Domain (OOD) inputs may run these systems into a problem. On the other side, a
labeled dataset is needed to train a model for Intent Detection in
task-oriented dialogue systems. The creation of a labeled dataset is
time-consuming and needs human resources. The purpose of this article is to
address mentioned problems. The task of identifying OOD/OOS inputs is named
OOD/OOS Intent Detection. Also, discovering new intents and pseudo-labeling of
OOD inputs is well known by Intent Discovery. In OOD intent detection part, we
make use of a Variational Autoencoder to distinguish between known and unknown
intents independent of input data distribution. After that, an unsupervised
clustering method is used to discover different unknown intents underlying
OOD/OOS inputs. We also apply a non-linear dimensionality reduction on OOD/OOS
representations to make distances between representations more meaning full for
clustering. Our results show that the proposed model for both OOD/OOS Intent
Detection and Intent Discovery achieves great results and passes baselines in
English and Persian languages.
