arXiv cs.CL
· Papers
CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising
arXiv:2607.28236v1 Announce Type: cross Abstract: Pre-trained language models have significantly improved sentence representation learning, yet their embedding remain sensitive to semantic preserving textual perturbations such as synonym substitution, masking and word dropout. This work proposes a lightweight Contrasti