@inproceedings{seonwoo-etal-2023-ranking,
    title = "Ranking-Enhanced Unsupervised Sentence Representation Learning",
    author = "Seonwoo, Yeon  and
      Wang, Guoyin  and
      Seo, Changmin  and
      Choudhary, Sajal  and
      Li, Jiwei  and
      Li, Xiang  and
      Xu, Puyang  and
      Park, Sunghyun  and
      Oh, Alice",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.879",
    doi = "10.18653/v1/2023.acl-long.879",
    pages = "15783--15798",
    abstract = "Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. Despite this progress, sentence encoders are still limited to using only an input sentence when predicting its semantic vector. In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence. Based on this finding, we propose a novel unsupervised sentence encoder, RankEncoder. RankEncoder predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus, as well as the input sentence itself. We evaluate RankEncoder on semantic textual benchmark datasets. From the experimental results, we verify that 1) RankEncoder achieves 80.07{\%} Spearman{'}s correlation, a 1.1{\%} absolute improvement compared to the previous state-of-the-art performance, 2) RankEncoder is universally applicable to existing unsupervised sentence embedding methods, and 3) RankEncoder is specifically effective for predicting the similarity scores of similar sentence pairs.",
}

Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. Despite this progress, sentence encoders are still limited to using only an input sentence when predicting its semantic vector. In this work, we show that the semantic meaning of a sentence is also determined by nearest-neighbor sentences that are similar to the input sentence. Based on this finding, we propose a novel unsupervised sentence encoder, RankEncoder. RankEncoder predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus, as well as the input sentence itself. We evaluate RankEncoder on semantic textual benchmark datasets. From the experimental results, we verify that 1) RankEncoder achieves 80.07{\%} Spearman{'}s correlation, a 1.1{\%} absolute improvement compared to the previous state-of-the-art performance, 2) RankEncoder is universally applicable to existing unsupervised sentence embedding methods, and 3) RankEncoder is specifically effective for predicting the similarity scores of similar sentence pairs.
Seonwoo, Yeon Wang, Guoyin Seo, Changmin Choudhary, Sajal Li, Jiwei Li, Xiang Xu, Puyang Park, Sunghyun Oh, Alice
Ranking-Enhanced Unsupervised Sentence Representation Learning