To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

State-of-the-art deep neural networks are still struggling to address the catastrophic
forgetting problem in continual learning. In this paper, we propose one simple paradigm …

Nowadays, real-world applications often face streaming data, which requires the learning
system to absorb new knowledge as data evolves. Continual Learning (CL) aims to achieve …

With the digitization of modern cities, large data volumes and powerful computational
resources facilitate the rapid update of intelligent models deployed in smart cities. Continual …

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Natural Language Generation (NLG) typically involves evaluating the generated text in
various aspects (eg, consistency and naturalness) to obtain a comprehensive assessment …

Traditional event detection methods require predefined event schemas. However, manually
defining event schemas is expensive and the coverage of schemas is limited. To this end …

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are
widely used in various intelligent applications. In this article, we provide a comprehensive …

The ability to learn new concepts sequentially is a major weakness for modern neural
networks, which hinders their use in non-stationary environments. Their propensity to fit the …

We study continual event extraction, which aims to extract incessantly emerging event
information while avoiding forgetting. We observe that the semantic confusion on event …