CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising

arXiv:2607.28236v2 Announce Type: replace Abstract: Pre-trained language models have significantly improved sentence representation learning, yet their embeddings remain sensitive to textual perturbations such as synonym substitution, masking, and word dropout. This work proposes a lightweight Contrastive Denoising Autoencoder (CDAE) that refines pre-trained BERT embeddings by jointly optimizing contrastive and reconstruction objectives to learn perturbation-invariant representations. We evaluate the proposed framework under multiple perturbation strategies and strengths, and compare it agains

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