Abstract
Few-shot language learners adapt knowledge from a pre-trained model to recognize novel classes from a few-labeled sentences. In such settings, fine-tuning a pre-trained language model can cause severe over-fitting. In this paper, we propose an Embedding Hallucination (EmbedHalluc) method, which generates auxiliary embedding-label pairs to expand the fine-tuning dataset. The hallucinator is trained by playing an adversarial game with the discriminator, such that the hallucinated embedding is indiscriminative to the real ones in the fine-tuning dataset. By training with the extended dataset, the language learner effectively learns from the diverse hallucinated embeddings to overcome the over-fitting issue. Experiments demonstrate that our proposed method is effective in a wide range of language tasks, outperforming current fine-tuning methods. Further, we show that EmbedHalluc outperforms other methods that address this over-fitting problem, such as common data augmentation, semi-supervised pseudo-labeling, and regularization.- Anthology ID:
- 2022.naacl-main.404
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5522–5530
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2022.naacl-main.404/
- DOI:
- 10.18653/v1/2022.naacl-main.404
- Cite (ACL):
- Yiren Jian, Chongyang Gao, and Soroush Vosoughi. 2022. Embedding Hallucination for Few-shot Language Fine-tuning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5522–5530, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Embedding Hallucination for Few-shot Language Fine-tuning (Jian et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2022.naacl-main.404.pdf
- Code
- yiren-jian/embedhalluc