Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning
Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Yuanmeng Yan, Weiran Xu · 2022
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system.Traditional softmaxbased confidence scores are susceptible to the overconfidence issue.In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.Further, given a small set of labeled OOD samples, we introduce an energy-based margin objective for supervised OOD detection to explicitly distinguish OOD samples from INDs.Comprehensive experiments and analysis prove our method helps disentangle confidence score distributions of IND and OOD data. 1