  Self-supervised learning approach like contrastive learning is attached great
attention in natural language processing. It uses pairs of training data
augmentations to build a classification task for an encoder with well
representation ability. However, the construction of learning pairs over
contrastive learning is much harder in NLP tasks. Previous works generate
word-level changes to form pairs, but small transforms may cause notable
changes on the meaning of sentences as the discrete and sparse nature of
natural language. In this paper, adversarial training is performed to generate
challenging and harder learning adversarial examples over the embedding space
of NLP as learning pairs. Using contrastive learning improves the
generalization ability of adversarial training because contrastive loss can
uniform the sample distribution. And at the same time, adversarial training
also enhances the robustness of contrastive learning. Two novel frameworks,
supervised contrastive adversarial learning (SCAL) and unsupervised SCAL
(USCAL), are proposed, which yields learning pairs by utilizing the adversarial
training for contrastive learning. The label-based loss of supervised tasks is
exploited to generate adversarial examples while unsupervised tasks bring
contrastive loss. To validate the effectiveness of the proposed framework, we
employ it to Transformer-based models for natural language understanding,
sentence semantic textual similarity and adversarial learning tasks.
Experimental results on GLUE benchmark tasks show that our fine-tuned
supervised method outperforms BERT$_{base}$ over 1.75\%. We also evaluate our
unsupervised method on semantic textual similarity (STS) tasks, and our method
gets 77.29\% with BERT$_{base}$. The robustness of our approach conducts
state-of-the-art results under multiple adversarial datasets on NLI tasks.
