Papers › Induction Networks for Few-Shot Text Classification
Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li, Xiaodan Zhu, Ping Jian, Jian Sun
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Text Classification | ODIC 10-way (10-shot) | Induction Networks | Accuracy | 81.64 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Text Classification | ODIC 10-way (5-shot) | Induction Networks | Accuracy | 78.27 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Text Classification | ODIC 5-way (10-shot) | Induction Networks | Accuracy | 88.49 | #1 of 1 | Archive leaderboard | report |
| Few-Shot Text Classification | ODIC 5-way (5-shot) | Induction Networks | Accuracy | 87.16 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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