Semantic matching for text classification with complex class descriptions (2023.emnlp-main)
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| Challenge: | Existing methods for text classification support zero-shot learning but not both . Existing approaches do not support zero or few-shot, and are insufficient for complex classes . |
| Approach: | They propose a method which rapidly adapts from seen classes to new/unseen ones . they use labels and complex class descriptions to perform zero- and few-shot learning . |
| Outcome: | The proposed method beats baselines on complex class descriptions by 22.48% . it also improves zero-shot learning by 4.29% . |
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