Recent mainstream event argument extraction methods process each event in
isolation, resulting in inefficient inference and ignoring the correlations
among multiple events. To address these limitations, here we propose a
multiple-event argument extraction model DEEIA (Dependency-guided Encoding and
Event-specific Information Aggregation), capable of extracting arguments from
all events within a document simultaneouslyThe proposed DEEIA model employs a
multi-event prompt mechanism, comprising DE and EIA modules. The DE module is
designed to improve the correlation between prompts and their corresponding
event contexts, whereas the EIA module provides event-specific information to
improve contextual understanding. Extensive experiments show that our method
achieves new state-of-the-art performance on four public datasets (RAMS,
WikiEvents, MLEE, and ACE05), while significantly saving the inference time
compared to the baselines. Further analyses demonstrate the effectiveness of
the proposed modules.

提出了一种多事件论证提取模型 DEEIA (Dependency-guided Encoding and Event-specific Information Aggregation)，该模型能够同时从文档中提取所有事件的论证，并能够在四个公共数据集上达到最新的最佳性能，并显著节省推理时间。

超越单事件提取：迈向高效的文档级多事件论证提取

Beyond Single-Event Extraction: Towards Efficient Document-Level  Multi-Event Argument Extraction

Event extraction is of practical utility in natural language processing. In
the real world, it is a common phenomenon that multiple events existing in the
same sentence, where extracting them are more difficult than extracting a
single event. Previous works on modeling the associations between events by
sequential modeling methods suffer a lot from the low efficiency in capturing
very long-range dependencies. In this paper, we propose a novel Jointly
Multiple Events Extraction (JMEE) framework to jointly extract multiple event
triggers and arguments by introducing syntactic shortcut arcs to enhance
information flow and attention-based graph convolution networks to model graph
information. The experiment results demonstrate that our proposed framework
achieves competitive results compared with state-of-the-art methods.

本文提出了一种新的 Jointly Multiple Events Extraction (JMEE) 框架，采用语法快捷弧增强信息流和基于注意力机制的图卷积网络建模图信息，实现了同时提取多个事件触发器和参数，结果显示该框架与现有方法竞争性强。