COFFEE: A Contrastive Oracle-Free Framework for Event Extraction
Meiru Zhang, Yixuan Su, Zaiqiao Meng, Zihao Fu, Nigel Collier · 2023
Event extraction is a complex 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 task setting, namely the Oracle-Free Event Extraction (OFEE) task, where only the input context is given, without any oracle information including event type, event ontology, or trigger word.To address this task, we propose a new framework, COF-FEE.This framework extracts events solely based on the document context, without referring to any oracle information.In particular, COFFEE introduces a contrastive selection model to refine the generated triggers and handle multi-event instances.Our proposed COF-FEE outperforms state-of-the-art approaches in the oracle-free setting of the event extraction task, as evaluated on two public variants of the ACE05 benchmark.The code used in our study has been made publicly available 1 .