Dynamic Memory Induction Networks for Few-Shot Text Classification

Ruiying Geng; Binhua Li; Yongbin Li; Jian Sun; Xiaodan Zhu

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arXiv · 2005.05727

Dynamic Memory Induction Networks for Few-Shot Text Classification

Ruiying Geng·Binhua Li·Yongbin Li·Jian Sun·Xiaodan Zhu

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Abstract

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.

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Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu. 2020-05-12. Dynamic Memory Induction Networks for Few-Shot Text Classification. https://arxiv.org/abs/2005.05727

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