  Contrastive self-supervised learning has become a prominent technique in
representation learning. The main step in these methods is to contrast
semantically similar and dissimilar pairs of samples. However, in the domain of
Natural Language Processing (NLP), the augmentation methods used in creating
similar pairs with regard to contrastive learning (CL) assumptions are
challenging. This is because, even simply modifying a word in the input might
change the semantic meaning of the sentence, and hence, would violate the
distributional hypothesis. In this review paper, we formalize the contrastive
learning framework, emphasize the considerations that need to be addressed in
the data transformation step, and review the state-of-the-art methods and
evaluations for contrastive representation learning in NLP. Finally, we
describe some challenges and potential directions for learning better text
representations using contrastive methods.
