Strengthened multiple correlation for multi-label few-shot intent detection

A challenging problem that arises in few-shot intent detection is the complexity of multiple intention (multi-label) detection. The prototypical network uses the mean value of support instances as label prototype, which cannot eliminate the interference among features of multiple labels, making the learned label prototypes deviate from the real label features. Meanwhile, regardless of the correlation with the label prototype, all the feature dimensions in the query instance are treated equally, which reduces the accuracy of similarity measurement between the query instance and the label prototype. In this paper, we propose a hybrid calculation of correlation for few-shot multi-label intent detection method (HCC-FSML) to overcome the problem. This method proposes an instance-level attention mechanism to focus on the instance representation with high correlation between support instances and positive labels, so as to improve the consistency between the label prototype and the real label features; In the similarity measurement, the feature-level attention mechanism is introduced to focus on the feature dimensions of query instance with high correlation with positive label prototype, so as to improve the accuracy of similarity measurement. The experiment shows that the proposed method achieves new state-of-the-art results in intent detection by focusing on typical features of instances and reducing multiple interference among labels.