Unsupervised Sentence Representation

Unsupervised sentence representation learning aims to create meaningful vector representations of sentences without relying on labeled data, a crucial step for many natural language processing tasks. Current research heavily utilizes contrastive learning, often within pre-trained language models like BERT, focusing on improvements such as better negative sampling strategies (e.g., clustering-based approaches) and mitigating issues like over-smoothing and sampling bias. These advancements lead to more accurate and robust sentence embeddings, improving performance on downstream tasks like semantic textual similarity. The resulting high-quality sentence representations have significant implications for various applications, including information retrieval, text classification, and machine translation.