Intent classification is a major task in spoken language understanding (SLU).
Since most models are built with pre-collected in-domain (IND) training
utterances, their ability to detect unsupported out-of-domain (OOD) utterances
has a critical effect in practical use. Recent works have shown that using
extra data and labels can improve the OOD detection performance, yet it could
be costly to collect such data. This paper proposes to train a model with only
IND data while supporting both IND intent classification and OOD detection. Our
method designs a novel domain-regularized module (DRM) to reduce the
overconfident phenomenon of a vanilla classifier, achieving a better
generalization in both cases. Besides, DRM can be used as a drop-in replacement
for the last layer in any neural network-based intent classifier, providing a
low-cost strategy for a significant improvement. The evaluation on four
datasets shows that our method built on BERT and RoBERTa models achieves
state-of-the-art performance against existing approaches and the strong
baselines we created for the comparisons.

该研究提出了一种使用领域正则化模块的方法，仅使用 In-domain 数据培训模型，支持 In-domain 意图分类和 Out-of-domain 检测，并在 4 个数据集上达到了最先进的性能。