Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU

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 is critical in practice. Recent works showed that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. In this paper, we propose to train a joint model only on IND training set to support both IND intent classification and OOD detection. Our method explicitly models a domain variable to learn the domain disentangled utterance representation, named DDM model. DDM can be used as a drop-in replacement for any deep neural intent classifier. To further improve OOD detection performance, we introduce confidence and feature based OOD detection methods to combine with DDM and BERT-based models. On all three benchmark SLU datasets and one in-house dataset, we show that our method built on BERT and RoBERTa models achieve the state-of-the-art performance against existing approaches as well as multiple BERT based strong baselines for both intent classification and OOD detection tasks.