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 …

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 …

Existing KG-augmented models for commonsense question answering primarily focus on
designing elaborate Graph Neural Networks (GNNs) to model knowledge graphs (KGs) …

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

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 …

Most previous studies of document-level event extraction mainly focus on building argument
chains in an autoregressive way, which achieves a certain success but is inefficient in both …

We solve the challenging document-level event extraction problem by proposing a joint
exaction methodology that can avoid inefficiency and error propagation issues in classic …

Event extraction (EE) is a crucial task aiming at extracting events from texts, which includes
two subtasks: event detection (ED) and event argument extraction (EAE). In this paper, we …

Event extraction aims to identify an event and then extract the arguments participating in the
event. Despite the great success in sentencelevel event extraction, events are more …