Few-shot text classification has extensive application where the sample collection is
expensive or complicated. When the penalty for classification errors is high, such as early …

Incremental learning plays a pivotal role in the context of online knowledge discovery, as it
encourages large models (LM) to learn and refresh knowledge continuously. Many …

Short text clustering is challenging since it takes imbalanced and noisy data as inputs.
Existing approaches cannot solve this problem well, since (1) they are prone to obtain …

Sequential labeling is a task predicting labels for each token in a sequence, such as Named
Entity Recognition (NER). NER tasks aim to extract entities and predict their labels given a …

Catastrophic forgetting poses a major challenge in continual learning where the old
knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to …

Sequential Recommendation (SR) has received increasing attention due to its ability to
capture user dynamic preferences. Recently, Contrastive Learning (CL) provides an …

Industrial control systems (ICSs) are getting integrated into cyber-physical systems (CPSs)
for a smarter and more energy-efficient society. As they organize the infrastructure of our …

Practical natural language processing (NLP) tasks are commonly long-tailed with noisy
labels. Those problems challenge the generalization and robustness of complex models …

This study introduces the Semantic Textual Similarity Pseudo-Label Semi-Supervised
Clustering (STSPL-SSC) framework. The STSPL-SSC framework is designed to tackle the …

The widespread of social media has led to the propagation of rumors, resulting in
detrimental effects on society. Existing rumor detection methods rely on labeled data and …