Sentence embedding, which aims to learn an effective representation of the sentence, is
beneficial for downstream tasks. By utilizing contrastive learning, most recent sentence …

Sentence representation learning is a crucial task in natural language processing, as the
quality of learned representations directly influences downstream tasks, such as sentence …

Embedding text sequences is a widespread requirement in modern language
understanding. Existing approaches focus largely on constant-size representations. This is …

User alignment can associate multiple social network accounts of the same user. It has
important research implications. However, the same user has various behaviors and friends …

By summarizing longer consumer health questions into shorter and essential ones, medical
question answering (MQA) systems can more accurately understand consumer intentions …

Document representation is the basis of language modeling. Its goal is to turn natural
language text that flows into a structured form that can be stored and processed by a …

Contrastive learning has been proven to be effective in learning better sentence
representations. However, to train a contrastive learning model, large numbers of labeled …

This paper improves contrastive learning for sentence embeddings from two perspectives:
handling dropout noise and addressing feature corruption. Specifically, for the first …

Recently, large language models (LLMs) have emerged as a groundbreaking technology
and their unparalleled text generation capabilities have sparked interest in their application …

The enhancement of unsupervised learning of sentence representations has been
significantly achieved by the utility of contrastive learning. This approach clusters the …