Summary of Label-template Based Few-shot Text Classification with Contrastive Learning, by Guanghua Hou et al.
Label-template based Few-Shot Text Classification with Contrastive Learning
by Guanghua Hou, Shuhui Cao, Deqiang Ouyang, Ning Wang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for few-shot text classification addresses limitations in traditional meta-learning approaches by fully utilizing class labels. A simple and effective approach embeds label templates into input sentences, guiding pre-trained models to generate more discriminative representations through semantic information. The scheme utilizes supervised contrastive learning to model interaction between support and query samples, replacing averaging with attention mechanisms to highlight vital semantic information. Experimental results on four datasets show significant performance enhancements and outperformed state-of-the-art models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new approach for few-shot text classification that makes better use of class labels. Instead of relying on differences between classes, this method puts label information directly into the sentences to help the model learn more about what’s important. This leads to better results and outperforms other methods. |
Keywords
» Artificial intelligence » Attention » Few shot » Meta learning » Supervised » Text classification
