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• W2975576859 abstract "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." @default.
• W2975576859 created "2019-10-03" @default.
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• W2975576859 date "2019-02-27" @default.
• W2975576859 modified "2023-09-23" @default.
• W2975576859 title "Induction Networks for Few-Shot Text Classification" @default.
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• W2975576859 doi "https://doi.org/10.48550/arxiv.1902.10482" @default.
• W2975576859 hasPublicationYear "2019" @default.
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