A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework

2026-08-24

AI Summary

本文提出一种统一生成式序列到序列框架，联合执行事件抽取子任务，通过微调预训练语言模型实现跨领域的可扩展性和泛化能力。

Abstract

Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative sequence-to-sequence framework that performs event extraction subtasks jointly and supports both pipeline and end-to-end configurations. We fine-tune pretrained language models on multiple event datasets across diverse domains, enabling a single model to retain domain-specific semantics while generalizing over large and evolving label spaces. We demonstrate these capabilities through a web-based application tailored for researchers and practitioners. The platform supports document upload, schema-aware event extraction, visualization of triggers and arguments, and comparison of different extraction configurations across domains.

Problem

• Event Extraction
• Cross-Domain Generalization
• Scalability

Innovation

• unified generative framework
• sequence-to-sequence
• cross-domain generalization
• pretrained language models