In the last decade, deep supervised learning has had tremendous success. However, its
flaws, such as its dependency on manual and costly annotations on large datasets and …

Semantic communication (SemCom) aims to convey the meaning behind a transmitted
message by transmitting only semantically-relevant information. This semantic-centric …

We present modality gap, an intriguing geometric phenomenon of the representation space
of multi-modal models. Specifically, we show that different data modalities (eg images and …

Text embeddings are useful features in many applications such as semantic search and
computing text similarity. Previous work typically trains models customized for different use …

In order to develop effective sequential recommenders, a series of sequence representation
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …

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 …

We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence
embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference …

Recent advancements of sequential deep learning models such as Transformer and BERT
have significantly facilitated the sequential recommendation. However, according to our …

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 …