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

TL;DR

This paper introduces DEEIA, a novel model for document-level multi-event argument extraction that processes all events simultaneously, improving efficiency and accuracy by capturing event correlations.

Contribution

The paper proposes DEEIA, a multi-event prompt-based model with DE and EIA modules, enabling efficient and accurate extraction of multiple events from documents.

Findings

Achieves state-of-the-art results on four datasets.

Significantly reduces inference time.

Effectively models event correlations.

Abstract

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.