Estimating the semantic similarity between text data is one of the challenging and open
research problems in the field of Natural Language Processing (NLP). The versatility of …

We introduce C-Pack, a package of resources that significantly advances the field of general
text embeddings for Chinese. C-Pack includes three critical resources. 1) C-MTP is a …

Text embeddings are commonly evaluated on a small set of datasets from a single task not
covering their possible applications to other tasks. It is unclear whether state-of-the-art …

High-quality text embedding is pivotal in improving semantic textual similarity (STS) tasks,
which are crucial components in Large Language Model (LLM) applications. However, a …

Background: Semantic textual similarity is a common task in the general English domain to
assess the degree to which the underlying semantics of 2 text segments are equivalent to …

We provide the first exploration of sentence embeddings from text-to-text transformers (T5).
Sentence embeddings are broadly useful for language processing tasks. While T5 achieves …

This paper presents SimCSE, a simple contrastive learning framework that greatly advances
state-of-the-art sentence embeddings. We first describe an unsupervised approach, which …

Learning high-quality sentence representations benefits a wide range of natural language
processing tasks. Though BERT-based pre-trained language models achieve high …

Pre-trained contextual representations like BERT have achieved great success in natural
language processing. However, the sentence embeddings from the pre-trained language …

Pre-training models such as BERT have achieved great success in many natural language
processing tasks. However, how to obtain better sentence representation through these pre …