Event extraction is a complex information extraction task that involves extracting events from unstructured text. Prior classification-based methods require comprehensive entity annotations for joint training, while newer generation-based methods rely on heuristic templates containing oracle information such as event type, which is often unavailable in real-world scenarios. In this study, we consider a more realistic setting of this task, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given without any oracle information, including event type, event ontology and trigger word. To solve this task, we propose a new framework, called COFFEE, which extracts the events solely based on the document context without referring to any oracle information. In particular, a contrastive selection model is introduced in COFFEE to rectify the generated triggers and handle multi-event instances. The proposed COFFEE outperforms state-of-the-art approaches under the oracle-free setting of the event extraction task, as evaluated on a public event extraction benchmark ACE05.

翻译：事件抽取是一项复杂的信息抽取任务，旨在从非结构化文本中抽取事件。以往基于分类的方法需要全面的实体标注进行联合训练，而新一代基于生成的方法依赖于包含先验信息（如事件类型）的启发式模板，但在实际场景中，此类先验信息通常不可获取。本研究考虑该任务更具现实性的设定——无先验信息事件抽取（OFEE）任务，即仅给定输入上下文，不提供任何先验信息（包括事件类型、事件本体和触发词）。为解决此问题，我们提出了一种名为COFFEE的新框架，该框架仅基于文档上下文抽取事件，无需参考任何先验信息。具体而言，COFFEE引入了一个对比选择模型，用于纠正生成的触发词并处理多事件实例。在公开事件抽取基准ACE05上的评估结果表明，所提出的COFFEE框架在事件抽取任务的无先验信息设定下优于现有最先进方法。