Detecting Out-of-Domain (OOD) or unknown intents from user queries is
essential in a task-oriented dialog system. Traditional softmax-based
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.\footnote{Our code is
available at https://github.com/pris-nlp/EMNLP2022-energy_for_OOD/.}

本文提出了一种简单而强大的基于能量的得分函数来检测超领域或未知意图的方法，同时介绍了一种基于能量的边界目标来区分超领域与内部领域，并通过全面的实验和分析证明了该方法有助于解开内部领域和超领域数据的置信度得分分布。