Pre-trained transformer models shine in many natural language processing tasks and
therefore are expected to bear the representation of the input sentence or text meaning …

Most sentence embedding techniques heavily rely on expensive human-annotated
sentence pairs as the supervised signals. Despite the use of large-scale unlabeled data, the …

Contrastive learning has been the dominant approach to train state-of-the-art sentence
embeddings. Previous studies have typically learned sentence embeddings either through …

The degree of semantic relatedness of two units of language has long been considered
fundamental to understanding meaning. Additionally, automatically determining relatedness …

One of the major outstanding questions in computational semantics is how humans integrate
the meaning of individual words into a sentence in a way that enables understanding of …

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 …

Abstract Word Mover's Distance (WMD) computes the distance between words and models
text similarity with the moving cost between words in two text sequences. Yet, it does not …

Computing the semantic similarity between two texts is crucial in various NLP tasks. For
more than a decade, a framework, known as Semantic Textual Similarity (STS) has been …

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