Label Hierarchical Structure-Aware Multi-Label Few-Shot Intent Detection via Prompt Tuning

Multi-label intent detection aims to recognize multiple user intents behind dialogue utterances. The diversity of user utterances and the scarcity of training data motivate multi-label few-shot intent detection. However, existing methods ignore the hybrid of verb and noun within an intent, which is essential to identify the user intent. In this paper, we propose a label hierarchical structure-aware method for multi-label few-shot intent detection via prompt tuning (LHS). Firstly, for the support data, we concatenate the original utterance with the label description generated by GPT-4 to obtain the utterance-level representation. Then we construct a multi-label hierarchical structure-aware prompt model to learn the label hierarchical information. To learn more discriminative class prototypes, we devise a prototypical contrastive learning method to pull the utterances close to their corresponding intent labels and away from other intent labels. Extensive experiments on two datasets demonstrate the superiority of our method.