Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning (2022)

Abstract: 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.

## introduction detecting out-of-domain (ood) or unknown intents from user queries is crucial to a task-oriented dialog system @xcite @xcite @xcite @xcite . it can avoid performing wrong operations and provide potential directions of future development when an input query falls outside the range of predefined intents. since the exact number of unknown intents in practical scenarios is hard to know and annotate, the lack of real ood examples makes it challenging to identify these samples in dialog systems. depending on whether labeled ood samples are available, previous ood detection work can be generally classified into two types: unsupervised @xcite @xcite @xcite @xcite zeng @xcite and supervised @xcite @xcite . the former firstly learn an in-domain (ind) intent classifier only using labeled ind data and then estimates the confidence score of a test query. for example, maximum softmax probability (msp) @xcite uses maximum softmax probability as the confidence score and regards an intent as ood if the score is below a fixed threshold. the assumption is that ood intents should produce a lower softmax probability than inds. however, neural networks can produce arbitrarily high softmax confidence even for such abnormal ood samples @xcite , as shown in fig 1&2 , which we call overconfidence. further, another distance-based method, gaussian discriminant analysis (gda) @xcite , is proposed to use the maximum mahalanobis distance @xcite to all in-domain classes centroids as the confidence score. compared to msp, gda gets better ood performance but requires expensive computation for complex mahalanobis distance. ## conclusion traditional softmax-based ood detection methods are susceptible to the overconfidence issue. therefore, we propose a novel energy-based score function to mitigate the issue of softmax overconfidence. to use labeled ood data, we further introduce an energy-based margin objective to explicitly distinguish energy score distributions of ood from ind. experiments and analysis confirm the effectiveness of our energy-based method for ood detection. for future work, we hope to explore theoretical concepts of energy and provide new guidance.