Papers by Myeong Soo Han
Simple Temperature Cool-down in Contrastive Framework for Unsupervised Sentence Representation Learning (2024.findings-eacl)
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| Challenge: | Existing studies have focused on the effectiveness of contrastive learning in deep learning. |
| Approach: | They propose a method to improve sentence representation of unsupervised contrastive learning by examining the role of temperature in VRL and SRL. |
| Outcome: | The proposed method improves representation of unsupervised contrastive learning by cooling the temperature of the representation space. |
Bootstrap Your Own PLM: Boosting Semantic Features of PLMs for Unsuperivsed Contrastive Learning (2024.findings-eacl)
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| Challenge: | Existing studies have shown that SimCSE significantly improves the performance of pretrained language models on the sentence representation benchmark. |
| Approach: | They propose a method called IFM which reduces the tendency of contrastive models for VRL to rely on feature-suppressing shortcut solutions. |
| Outcome: | The proposed method reduces the tendency of contrastive models for VRL to rely on feature-suppressing shortcut solutions. |