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.
After revision: Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, 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.