Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

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 …

The challenge of information extraction (IE) lies in the diversity of label schemas and the
heterogeneity of structures. Traditional methods require task-specific model design and rely …

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 …

Recent work on Event Extraction has reframed the task as Question Answering (QA), with
promising results. The advantage of this approach is that it addresses the error propagation …

Event extraction is typically modeled as a multi-class classification problem where event
types and argument roles are treated as atomic symbols. These approaches are usually …

Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as
Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few …

Abstract Information extraction tasks such as event extraction require an in-depth
understanding of the output structure and sub-task dependencies. They heavily rely on task …

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that
can be applied to a large variety of newsworthy scenarios. The framework consists of two …

Deep learning models have achieved state-of-the-art performance for text classification in
the last two decades. However, this has come at the expense of models becoming less …