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- W4387426939 abstract "Benefiting from Pre-trained Language Model (PLM), Event Argument Extraction (EAE) methods have achieved SOTA performance in general scenarios of Event Extraction (EE). However, with increasing concerns and regulations on data privacy, aggregating distributed data among different institutions in some privacy-sensitive territories (e.g., medical record analysis, financial statement analysis, etc.) becomes very difficult, and it’s hard to train an accurate EAE model with limited local data. Federated Learning (FL) provides promising methods for a large number of clients to collaboratively learn a shared global model without the need to exchange privacy-sensitive data. Therefore, we propose a privacy-preserving EAE method named FedEAE based on FL to solve the current difficulties. To better adapt to federated scenarios, we design a dataset named FedACE generated from the ACE2005 dataset under IID and Non-IID for our experiments. Extensive experiments show that FedEAE achieves promising performance compared to existing baselines, thus validates the effectiveness of our method. To the best of our knowledge, FedEAE is the first to apply FL in the EAE task." @default.
- W4387426939 created "2023-10-08" @default.
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- W4387426939 date "2023-01-01" @default.
- W4387426939 modified "2023-10-18" @default.
- W4387426939 title "FedEAE: Federated Learning Based Privacy-Preserving Event Argument Extraction" @default.
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- W4387426939 doi "https://doi.org/10.1007/978-3-031-44696-2_26" @default.
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