Papers by Amalie Pauli
Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification (2023.emnlp-main)
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| Challenge: | Existing methods for fewshot text classification require substantial computing power and data. |
| Approach: | They propose an efficient method to add task and label information to a sentence transformer model by contrastive learning and a triplet loss to enforce training instances to be closest to their own textual semantic label information. |
| Outcome: | The proposed method achieves strong performance in data-sparse scenarios compared to existing methods across SST-5, Emotion detection, and AG News data even with just two examples per class. |