Papers with NT-Xent loss
Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning (2022.findings-emnlp)
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| Challenge: | Existing contrastive methods for learning universal sentence embeddings have limitations due to their over-parameterization and poor performance under domain shift settings. |
| Approach: | They propose to integrate an Energy-based Hinge loss to enhance the pairwise discriminative power of contrastive learning for sentence embeddings by combining PLMs with energy-based learning. |
| Outcome: | Empirical results show that the proposed method improves on seven standard semantic textual similarity tasks and a domain-shifted STS task. |