Papers with Unsupervised sentence representation learning
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)
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Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
| Challenge: | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences . |
| Approach: | They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework. |
| Outcome: | The proposed approach performs better over state-of-the-art models on STS and TR tasks. |
Ranking-Enhanced Unsupervised Sentence Representation Learning (2023.acl-long)
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Yeon Seonwoo, Guoyin Wang, Changmin Seo, Sajal Choudhary, Jiwei Li, Xiang Li, Puyang Xu, Sunghyun Park, Alice Oh
| Challenge: | Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. |
| Approach: | They propose a novel unsupervised sentence encoder, RankEncoder, which predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus. |
| Outcome: | The proposed unsupervised sentence encoder achieves 80.07% Spearman’s correlation, a 1.1% improvement over the previous state-of-the-art system. |