Few-shot Learning for Multi-label Intent Detection

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 oneshot and five-shot settings. Data and code are available at https: //github. com/AtmaHou/FewShotMultiLabel.

Authors

• Yutai Hou Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Yongkui Lai
• Yushan Wu
• Wanxiang Che Research Center for SCIR, Harbin Institute of Technology, Harbin, China
• Ting Liu Tsinghua University

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