Unsupervised Sentence Representation Learning

Unsupervised sentence representation learning aims to automatically learn meaningful vector representations of sentences without relying on labeled data, a crucial step for various NLP tasks. Current research heavily focuses on contrastive learning methods, often enhanced by techniques like data augmentation (e.g., dropout, word shuffling), ranking-based approaches, and clustering to improve the quality and discriminative power of learned embeddings. These advancements are significant because effective unsupervised sentence representations enable improved performance in downstream tasks like semantic textual similarity and cross-lingual transfer, reducing the reliance on expensive labeled datasets.