Continual Named Entity Recognition (CNER) is a burgeoning area, which involves updating
an existing model by incorporating new entity types sequentially. Nevertheless, continual …

Incremental Learning (IL) has been a long-standing problem in both vision and Natural
Language Processing (NLP) communities. In recent years, as Pre-trained Language Models …

Abstract Continual Named Entity Recognition (CNER) is dedicated to sequentially learning
new entity types while mitigating catastrophic forgetting of old entity types. Traditional CNER …

Continual named entity recognition requires models to be continuously updated to
recognize new entity types while retaining learned knowledge. In this task, the inherent …

The incremental sequence labeling task involves continuously learning new classes over
time while retaining knowledge of the previous ones. Our investigation identifies two …

Federated Named Entity Recognition (FNER) boosts model training within each local client
by aggregating the model updates of decentralized local clients, without sharing their private …

As the applications of large language models (LLMs) expand across diverse fields, the
ability of these models to adapt to ongoing changes in data, tasks, and user preferences …

Abstract Class Incremental Named Entity Recognition (CINER) needs to learn new entity
classes without forgetting old entity classes under the setting where the data only contain …

Continual Learning for Named Entity Recognition (CL-NER) is designed to train models
capable of adapting to evolving data by continuously introducing new entity types. This …