Virtual Augmentation Supported Contrastive Learning of Sentence Representations (2022.findings-acl)
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| Challenge: | Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. |
| Approach: | They propose a virtual augmentation supported Contrastive Learning of sentence representations . they approximate the neighborhood of an instance via its K-nearest in-batch neighbors . |
| Outcome: | The proposed model outperforms existing methods on a wide range of downstream tasks. |
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