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Status: Unchecked ×Keyword: sentence representation learning ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Self-Guided Contrastive Learning for BERT Sentence Representations

   Kim, Yoo and Lee · arXiv (Cornell University) · 2021

   The paper proposes a self-guided contrastive learning method to improve BERT sentence embeddings, and reports it beats competitive baselines on varied sentence tasks, runs efficiently and holds up under domain shift.

   Unchecked2 claims
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   1. UncheckedThe authors' method fine-tunes BERT without labels or data augmentation, so the standard [CLS] token embedding can serve as a sentence vector.“Our method fine-tunes BERT in a self-supervised fashion, does not rely on data augmentation, and enables the usual [CLS] token embeddings to function as sentence vectors.”
   2. UncheckedThe authors report that their BERT sentence-embedding method is efficient when used for inference and robust to shifts in the type of text.“We also show it is efficient at inference and robust to domain shifts.”