Video Summary

The paper proposes a new method for training language models, which is based on the idea of self-supervised learning. The method is called "Contrastive Learning for Language Representations" (CLLR). CLLR works by training a language model to predict whether two sentences are paraphrases of each other. This training objective encourages the language model to learn representations of words and phrases that are useful for distinguishing between paraphrases. The paper evaluates CLLR on a variety of natural language processing tasks, and shows that it achieves state-of-the-art results on most of them.