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Status: Unchecked ×Keyword: SimCSE ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings

   Chuang, Dangovski, Luo et al. · arXiv (Cornell University) · 2022

   The paper proposes DiffCSE, an unsupervised contrastive framework that learns sentence embeddings sensitive to edits made by a masked language model, and reports state-of-the-art results.

   Unchecked2 claims
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   1. UncheckedDiffCSE is reported to beat other unsupervised sentence-embedding methods, ahead of unsupervised SimCSE by 2.3 absolute points on semantic similarity tasks.“Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic textual similarity tasks.”
   2. UncheckedThe authors show that DiffCSE is an instance of equivariant contrastive learning, which is insensitive to some augmentations and sensitive to "harmful" ones.“We show that DiffSCE is an instance of equivariant contrastive learning (Dangovski et al., 2021), which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other "harmful" types of augmentations.”