Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

Event extraction requires high-quality expert human annotations, which are usually
expensive. Therefore, learning a data-efficient event extraction model that can be trained …

Large language models have shown astonishing performance on a wide range of reasoning
tasks. In this paper, we investigate whether they could reason about real-world events and …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

This paper reviews the state-of-the-art of language models architectures and strategies for"
complex" question-answering (QA, CQA, CPS) with a focus on hybridization. Large …

Event extraction has gained considerable interest due to its wide-ranging applications.
However, recent studies draw attention to evaluation issues, suggesting that reported scores …

Understanding temporal commonsense concepts, such as times of occurrence and
durations is crucial for event-centric language understanding. Reasoning about such …

Context: The dramatically increasing number of vulnerabilities makes manual vulnerability
analysis increasingly more difficult. Automatic extraction of vulnerability information can help …

Recent works in Event Argument Extraction (EAE) have focused on improving model
generalizability to cater to new events and domains. However, standard benchmarking …

Event reasoning is a fundamental ability that underlies many applications. It requires event
schema knowledge to perform global reasoning and needs to deal with the diversity of the …