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 …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Geospatial technologies are becoming increasingly essential in our world for a wide range
of applications, including agriculture, urban planning, and disaster response. To help …

Identifying temporal and subevent relationships between different events (ie, event relation
extraction) is an important step towards event-centric natural language processing, which …

Pre-trained models are commonly used in Continual Learning to initialize the model before
training on the stream of non-stationary data. However, pre-training is rarely applied during …

Modelling and understanding time remains a challenge in contemporary video
understanding models. With language emerging as a key driver towards powerful …

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart
the process over again once new data becomes available. A much cheaper and more …

This paper focuses on extracting temporal and parent–child relationships between news
events in social news. Previous methods have proved that syntactic features are valid …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

We present TIMERS-a TIME, Rhetorical and Syntactic-aware model for document-level
temporal relation classification in the English language. Our proposed method leverages …