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.
@article{arxiv.2005.05727,
title = {Dynamic Memory Induction Networks for Few-Shot Text Classification},
author = {Ruiying Geng and Binhua Li and Yongbin Li and Jian Sun and Xiaodan Zhu},
journal= {arXiv preprint arXiv:2005.05727},
year = {2020}
}