Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis
(ABSA), aiming to analyze and understand people's opinions at the aspect level, has been …

In contrast to earlier ABSA studies primarily concentrating on individual sentiment
components, recent research has ventured into more complex ABSA tasks encompassing …

We propose Corder, a self-supervised contrastive learning framework for source code
model. Corder is designed to alleviate the need of labeled data for code retrieval and code …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Pre-trained Language Models (PLMs) have achieved remarkable performance for various
language understanding tasks in IR systems, which require the fine-tuning process based …

Sentiment Quantification aims to detect the overall sentiment polarity of users from a set of
reviews corresponding to a target. Existing methods equally treat and aggregate individual …

Reconstruction methods based on deep learning have greatly shortened the data
acquisition time of magnetic resonance imaging (MRI). However, these methods typically …

Time-series representation learning is crucial for extracting meaningful representations from
time-series data with temporal dynamics and sparse labels. Contrastive learning, a powerful …

Through minimization of an appropriate loss function such as the InfoNCE loss, contrastive
learning (CL) learns a useful representation function by pulling positive samples close to …