Papers by Ruikun Luo
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene (2021.findings-acl)
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| Challenge: | Existing approaches to fine-tune pre-trained models to downstream tasks are limited by labeled examples. |
| Approach: | They propose to apply post-training on unlabeled task data before fine-tuning by contrastive learning that considers either token-level or sequence-level similarity. |
| Outcome: | Empirical results show that contrastive masked language modeling surpasses other methods in few-shot settings without the need for data augmentation. |