论文标题
部分可观测时空混沌系统的无模型预测
The Open-World Lottery Ticket Hypothesis for OOD Intent Classification
论文作者
论文摘要
大多数现有的室外（OOD）意图分类方法都取决于广泛的辅助OOD语料库或特定的培训范式。但是，它们在基本原则中的发展欠发达，即模型应该区分对内和域外意图的信心。在这项工作中，我们阐明了OOD模型过度自信的基本原因，并证明可以通过修剪过度参数化模型来发现校准的子网。子网提供的校准置信度可以更好地区分内域和外域，这对于几乎所有事后方法都可能是一个好处。除了带来基本的见解外，我们还将彩票假设扩展到开放世界的情况。我们对四个现实世界数据集进行了广泛的实验，以证明我们的方法可以与一套竞争基线相比，可以建立一致的改进。
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.
