MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for text classification fail to generalize to unseen classes with very few labeled text instances per class. |
| Approach: | They propose a meta-learning method which performs instance-wise comparison followed by aggregation to generate class-wise matching vectors instead of prototype learning. |
| Outcome: | Experiments show that the proposed method outperforms existing methods under both the standard and generalized FSL settings. |
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