SimCSE

AbstractThis paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself …...

AbstractContrastive learning has shown great potential in unsupervised sentence embedding tasks, e.g., SimCSE (CITATION).However, these existing solutions are heavily affected by superficial features like the length of sentences or syntactic …...

AbstractContrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation …...

AbstractWe propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analysis the drawback of current sentence embedding from original BERT and find that it is …...

AbstractCurrently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language models (PLMs) and contrastive learning have achieved promising …...