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

• URL: http://arxiv.org/abs/2405.01884v2
• Date: Sun, 16 Jun 2024 13:26:37 GMT
• Title: Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
• Authors: Wanlong Liu, Li Zhou, Dingyi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen,
• Abstract summary: We propose a multiple-event argument extraction model DEEIA. It is capable of extracting arguments from all events within a document simultaneously. Our method achieves new state-of-the-art performance on four public datasets.
• Score: 19.51890490853855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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