Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
arxiv(2024)
摘要
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.
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