  Most existing methods of Out-of-Domain (OOD) intent classification rely on
extensive auxiliary OOD corpora or specific training paradigms. However, they
are underdeveloped in the underlying principle that the models should have
differentiated confidence in In- and Out-of-domain intent. In this work, we
shed light on the fundamental cause of model overconfidence on OOD and
demonstrate that calibrated subnetworks can be uncovered by pruning the
overparameterized model. Calibrated confidence provided by the subnetwork can
better distinguish In- and Out-of-domain, which can be a benefit for almost all
post hoc methods. In addition to bringing fundamental insights, we also extend
the Lottery Ticket Hypothesis to open-world scenarios. We conduct extensive
experiments on four real-world datasets to demonstrate our approach can
establish consistent improvements compared with a suite of competitive
baselines.
