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Most existing methods of Out-of-Domain (OOD) intent classification rely onextensive auxiliary OOD corpora or specific training paradigms. However, theyare underdeveloped in the underlying principle that the models should havedifferentiated confidence in In- and Out-of-domain intent. In this work, weshed light on the fundamental cause of model overconfidence on OOD anddemonstrate that calibrated subnetworks can be uncovered by pruning theoverparameterized model. Calibrated confidence provided by the subnetwork canbetter distinguish In- and Out-of-domain, which can be a benefit for almost allpost hoc methods. In addition to bringing fundamental insights, we also extendthe Lottery Ticket Hypothesis to open-world scenarios. We conduct extensiveexperiments on four real-world datasets to demonstrate our approach canestablish consistent improvements compared with a suite of competitivebaselines.

The Open-World Lottery Ticket Hypothesis for OOD Intent Classification