Dynamic Memory Induction Networks for Few-Shot Text Classification

This paper proposes Dynamic Memory Induction Networks (DMIN) for few-short text classification. The model develops a dynamic routing mechanism over static memory, enabling it to better adapt to unseen classes, a critical capability for few-short classification. The model also expands the induction process with supervised learning weights and query information to enhance the generalization ability of meta-learning. The proposed model brings forward the state-of-the-art performance significantly by 2~4% improvement on the miniRCV1 and ODIC datasets. Detailed analysis is further performed to show how the proposed network achieves the new performance.