Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

In this paper, we propose an effective yet efficient model PAIE for both sentence-level and
document-level Event Argument Extraction (EAE), which also generalizes well when there is …

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 studies have shown the effectiveness of retrieval augmentation in many generative
NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

Document-level event extraction (DEE) is indispensable when events are described
throughout a document. We argue that sentence-level extractors are ill-suited to the DEE …

Document-level event argument extraction poses new challenges of long input and cross-
sentence inference compared to its sentence-level counterpart. However, most prior works …

Most previous studies aim at extracting events from a single sentence, while document-level
event extraction still remains under-explored. In this paper, we focus on extracting event …

Textual documents serve as representations of discussions on a variety of subjects. These
discussions can vary in length and may encompass a range of events or factual information …