Cluster Validation Indices as Self-Supervised Objectives for Text Representation Learning
Self-supervised fine-tuning refines the embedding space of a pretrained language encoder without labels. However, the commonly used approaches are computationally expensive. Specifically, contrastive learning-based methods need multiview data and in-batch negative examples, while negative-free approaches require auxili...
Kishor Kumar Bhaumik, Nícolas Roque dos Santos, Neil Shah et al.
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