Most existing methods of Out-of-Domain (OOD) intent classification, which
rely on extensive auxiliary OOD corpora or specific training paradigms, are
underdeveloped in the underlying principle that the models should have
differentiated confidence in In- and Out-of-domain intent. In this work, we
demonstrate that calibrated subnetworks can be uncovered by pruning the
(poor-calibrated) overparameterized model. Calibrated confidence provided by
the subnetwork can better distinguish In- and Out-of-domain. Furthermore, we
theoretically bring new insights into why temperature scaling can differentiate
In- and Out-of-Domain intent and empirically extend the Lottery Ticket
Hypothesis to the open-world setting. Extensive experiments on three real-world
datasets demonstrate our approach can establish consistent improvements
compared with a suite of competitive baselines.

本研究提出了一种用于 Out-of-Domain 意图分类的新方法，即基于校准的子网络，该方法通过裁剪超参数化模型中的不良校准部分，发现校准的子网络以更好地区分 In 和 Out-of-domain，因此在三个真实数据集上与其他竞争性基线相比取得一致改进。