Cropping outperforms dropout as an augmentation strategy for self-supervised training of text embeddings

Rita González-Márquez, Philipp Berens, Dmitry Kobak

Text embeddings, i.e. vector representations of entire texts, play an important role in many NLP applications, such as retrieval-augmented generation, clustering, or visualizing co…