  This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot
text classification. The model utilizes dynamic routing to provide more
flexibility to memory-based few-shot learning in order to better adapt the
support sets, which is a critical capacity of few-shot classification models.
Based on that, we further develop induction models with query information,
aiming to enhance the generalization ability of meta-learning. The proposed
model achieves new state-of-the-art results on the miniRCV1 and ODIC dataset,
improving the best performance (accuracy) by 2~4%. Detailed analysis is further
performed to show the effectiveness of each component.
