统一生成框架实现跨领域事件抽取，支持端到端与流水线两种模式。
A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework
- 采用统一生成式序列到序列框架，联合完成事件检测与论元抽取。
- 单模型在多领域数据上微调，可同时保留领域特征并泛化至新标签空间。
- 提供可视化网页平台，支持文档上传、配置对比与跨域结果分析。
事件抽取是信息抽取的基础任务。以往方法常将事件检测与论元抽取分离，或依赖特定数据集设计，限制了可扩展性与跨领域泛化能力。我们提出一种统一的生成式序列到序列框架，可联合执行事件抽取子任务，并支持流水线与端到端两种配置。通过在多个跨领域事件数据集上微调预训练语言模型，使单一模型既能保持领域特异性语义，又能泛化至大规模且不断演化的标签空间。我们通过一个面向研究者与实践者的网络应用展示了这些能力：平台支持文档上传、基于模式的事件抽取、触发词与论元的可视化，以及跨领域的不同抽取配置比较。
原文摘要 · Abstract (English)
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.
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