Data-driven models have been widely used in building heating load prediction, but often fail
when facing limited data. Previous studies have shown transfer learning can assist model …

Few-shot intent detection is a challenging task. Most existing methods only focus on
acquisition of generalization knowledge in known classes, or on the adaptation situation of …

Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text
classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and …

In this short paper we propose a data augmentation method for intent detection in zero-
resource domains. Existing data augmentation methods rely on few labelled examples for …

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable
performance in scenarios characterized by a scarcity of labeled data during the training …

The previous adversarial training models failed to pay attention to the influence of the
changing gradient of the loss function in the current training on the model. The perturbation …