Dynamic Memory Induction Networks for Few-Shot Text Classification

• URL: http://arxiv.org/abs/2005.05727v1
• Date: Tue, 12 May 2020 12:41:14 GMT
• Title: Dynamic Memory Induction Networks for Few-Shot Text Classification
• Authors: Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu
• Abstract summary: This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The proposed model achieves new state-of-the-art results on the miniRCV1 and ODIC dataset, improving the best performance (accuracy) by 24%.
• Score: 84.88381813651971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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