Decoupling Representation and Knowledge for Few-Shot Intent
  Classification and Slot Filling
Jie Han Yixiong Zou Haozhao Wang Jun Wang Wei Liu Yao Wu Tao Zhang Ruixuan Li
Few-shot intent classification and slot filling are important but challenging
tasks due to the scarcity of finely labeled data. Therefore, current works
first train a model on source domains with sufficiently labeled data, and then
transfer the model to target domains where only rarely labeled data is
available. However, experience transferring as a whole usually suffers from
gaps that exist among source domains and target domains. For instance,
transferring domain-specific-knowledge-related experience is difficult. To
tackle this problem, we propose a new method that explicitly decouples the
transferring of general-semantic-representation-related experience and the
domain-specific-knowledge-related experience. Specifically, for
domain-specific-knowledge-related experience, we design two modules to capture
intent-slot relation and slot-slot relation respectively. Extensive experiments
on Snips and FewJoint datasets show that our method achieves state-of-the-art
performance. The method improves the joint accuracy metric from 27.72% to
42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.