Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system. Previous unsupervised OOD detection methods only extract discriminative features of different in-domain intents while supervised counterparts can directl
y distinguish OOD and in-domain intents but require extensive labeled OOD data. To combine the benefits of both types, we propose a self-supervised contrastive learning framework to model discriminative semantic features of both in-domain intents and OOD intents from unlabeled data. Besides, we introduce an adversarial augmentation neural module to improve the efficiency and robustness of contrastive learning. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin.
This research invistigates Althaalbi explanation as well as the
explanation of other interpretors in specifying the antecedent
in some Quran Verses. The research introduces their
arguments, discussions and studies after summerizing them.
Finally, the research adopts one of the present opinions and
provides the reasons for this selection.