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Event extraction (EE) is the task of identifying interested event mentionsfrom text. Conventional efforts mainly focus on the supervised setting.However, these supervised models cannot generalize to event types out of thepre-defined ontology. To fill this gap, many efforts have been devoted to thezero-shot EE problem. This paper follows the trend of modeling event-typesemantics but moves one step further. We argue that using the static embeddingof the event type name might not be enough because a single word could beambiguous, and we need a sentence to define the type semantics accurately. Tomodel the definition semantics, we use two separate transformer models toproject the contextualized event mentions and corresponding definitions intothe same embedding space and then minimize their embedding distance viacontrastive learning. On top of that, we also propose a warming phase to helpthe model learn the minor difference between similar definitions. We name ourapproach Zero-shot Event extraction with Definition (ZED). Experiments on theMAVEN dataset show that our model significantly outperforms all previouszero-shot EE methods with fast inference speed due to the disjoint design.Further experiments also show that ZED can be easily applied to the few-shotsetting when the annotation is available and consistently outperforms baselinesupervised methods.

Efficient Zero-shot Event Extraction with Context-Definition Alignment