Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world
knowledge. This fact has sparked the interest of the community in quantifying the amount of …

In an ever-evolving world, the dynamic nature of knowledge presents challenges for
language models that are trained on static data, leading to outdated encoded information …

Time is an important aspect of documents and is used in a range of NLP and IR tasks. In this
work, we investigate methods for incorporating temporal information during pre-training to …

In the evolving field of Natural Language Processing, understanding the temporal context of
text is increasingly crucial. This study investigates methods to incorporate temporal …

The self-supervised objective of masking-and-predicting has led to promising performance
gains on a variety of downstream tasks. However, while most approaches randomly mask …

Microblogging platforms can provide novel, attractive opportunities for communicating and
disseminating content about important events from the past. We propose a novel framework …