Induction Networks for Few-Shot Text Classification
This addresses the challenge of adapting text classification to new classes with limited data, offering a novel approach for applications like sentiment analysis and intent recognition.
The paper tackles the problem of few-shot text classification by proposing an Induction Network that learns generalized class-wise representations to overcome issues with sample-wise comparisons, achieving significant improvements over state-of-the-art methods on sentiment and dialogue intent datasets.
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to simulate the few-shot task, in which new queries are compared to a small support set at the 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 a generalized class-wise representation, by innovatively leveraging the dynamic routing algorithm in meta-learning. In this way, we find the model is able to induce and generalize better. We evaluate the proposed 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, the proposed model significantly outperforms the existing state-of-the-art approaches, proving the effectiveness of class-wise generalization in few-shot text classification.