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.

提出一种名为 COFFEE 的新框架，其使用对比选择模型来矫正产生的触发器、处理多事件实例，从而仅基于文档上下文提取事件，解决了不包含事件类型、事件本体以及触发词等 Oracle 信息的 Oracle-Free Event Extraction 任务，评估结果表明该方法优于现有技术。