In this paper, we study the few-shot multi-label classification for user intent detection. For multi-label intent detection, state-of-the-art work estimates label-instance relevance scores and uses a threshold to select multiple associated intent labels. To determine appropriate thresholds with only a few examples, we first learn universal thresholding experience on data-rich domains, and then adapt the thresholds to certain few-shot domains with a calibration based on nonparametric learning. For better calculation of label-instance relevance score, we introduce label name embedding as anchor points in representation space, which refines representations of different classes to be well-separated from each other. Experiments on two datasets show that the proposed model significantly outperforms strong baselines in both one-shot and five-shot settings.

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a.few-shot).Few-shot slot tagging faces a unique challenge compared to the other fewshot classification problems as it calls for modeling the dependencies between labels.But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets.To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores.In the few-shot setting, the emission score of CRF can be calculated as a word's similarity to the representation of each label.To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the stateof-the-art few-shot classification model -Tap-Net, by leveraging label name semantics in representing labels.Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. 1