论文标题
数量射击文本分类的动态记忆感应网络
Dynamic Memory Induction Networks for Few-Shot Text Classification
论文作者
论文摘要
本文提出了动态内存感应网络（DMIN），以进行几次播种文本分类。该模型利用动态路由来为基于内存的几次学习提供更大的灵活性，以便更好地调整支持集，这是几个射击分类模型的关键能力。基于此，我们进一步开发了具有查询信息的归纳模型，旨在增强元学习的概括能力。提出的模型在MinIRCV1和ODIC数据集上实现了新的最新结果，将最佳性能（精度）提高了2〜4％。进一步进行详细的分析以显示每个组件的有效性。
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.
