Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies often use meta learning to simulate the few-shot task, in which new queries are compared to a small support set on a sample-wise level. However, this sample-wise comparison may be severely disturbed by the various expressions in the same class. Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries. In this paper, we propose a novel Induction Network to learn such generalized class-wise representations, innovatively combining the dynamic routing algorithm with the typical meta learning framework. In this way, our model is able to induce from particularity to university, which is a more human-like learning approach. We evaluate our model on a well-studied sentiment classification dataset (English) and a real-world dialogue intent classification dataset (Chinese). Experiment results show that, on both datasets, our model significantly outperforms existing state-of-the-art models and improves the average accuracy by more than 3%, which proves the effectiveness of class-wise generalization in few-shot text classification.

本文提出了一种基于归纳网络的元学习算法来实现少样本文本分类任务中的类别泛化，通过将动态路由算法与元学习相结合，学习出每个类别的泛化表示，并将其与新的查询样本进行比较，在英文情感分类和中文对话意图分类任务中验部，所获得的实验结果显示，该模型在泛化类别的表现上显著优于现有的最先进的方法。

少样本文本分类的归纳网络