Before revision: We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations , a self-supervised method for learning universal sentence embeddings that transfer to a wide variety of natural language processing (NLP) tasks. Our objective leverages recent advances in deep metric learning (DML) and has the advantage of being conceptually simple and easy to implement, requiring no specialized architectures or labelled training data.
After revision: We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations . Inspired by recent advances in deep metric learning (DML) and has the advantage of being conceptually simple and easy to implement, requiring no specialized architectures or labelled training data.