Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
<|reference_start|>Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction: 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.<|reference_end|>
@article{liu2024beyond,
    title={Beyond Single-Event Extraction: Towards Efficient Document-Level
  Multi-Event Argument Extraction},
    author={Wanlong Liu, Li Zhou, Dingyi Zeng, Yichen Xiao, Shaohuan Cheng, Chen
  Zhang, Grandee Lee, Malu Zhang, Wenyu Chen},
    journal={arXiv preprint arXiv:2405.01884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01884},
    primaryClass={cs.CL}
}