Abstract
Few-shot classification is a powerful technique, but training requires substantial computing power and data. We propose an efficient method with small model sizes and less training data with only 2-8 training instances per class. Our proposed method, AncSetFit, targets low data scenarios by anchoring the task and label information through sentence embeddings in fine-tuning a Sentence Transformer model. It uses contrastive learning and a triplet loss to enforce training instances of a class to be closest to its own textual semantic label information in the embedding space - and thereby learning to embed different class instances more distinct. AncSetFit obtains 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.- Anthology ID:
- 2023.emnlp-main.692
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11254–11264
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.692
- DOI:
- 10.18653/v1/2023.emnlp-main.692
- Cite (ACL):
- Amalie Pauli, Leon Derczynski, and Ira Assent. 2023. Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11254–11264, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Anchoring Fine-tuning of Sentence Transformer with Semantic Label Information for Efficient Truly Few-shot Classification (Pauli et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.emnlp-main.692.pdf