Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. In this work, we take a fresh look at event extraction and model it as a grounding problem. We design a transferable neural architecture, mapping event mentions and types jointly into a shared semantic space using structural and compositional neural networks, where the type of each event mention can be determined by the closest of all candidate types . By leveraging (1)~available manual annotations for a small set of existing event types and (2)~existing event ontologies, our framework applies to new event types without requiring additional annotation. Experiments on both existing event types (e.g., ACE, ERE) and new event types (e.g., FrameNet) demonstrate the effectiveness of our approach. \textit{Without any manual annotations} for 23 new event types, our zero-shot framework achieved performance comparable to a state-of-the-art supervised model which is trained from the annotations of 500 event mentions.

翻译：以往的大多数事件提取研究都大量依赖由附加说明的事件提到的特征,因此不做说明就无法应用于新的事件类型。在这项工作中,我们重新审视事件提取,并将事件模型作为基底问题。我们设计了可转移的神经结构,制图事件提及,并用结构和构成性神经网络共同划入一个共同的语义空间,每个事件的类型可以由所有候选类型中最接近的类型来决定。通过利用(1) 现有小类事件现有手语说明和(2) 现有事件目录,我们的框架适用于新事件类型,而无需额外说明。对现有事件类型(如ACE、ERE)和新事件类型的实验(如FramtNet)都显示了我们的方法的有效性。\textit{在没有任何手动说明的情况下, 23个新事件类型,我们的零点框架取得了与从500事件说明中培训的状态监督模式相类似的业绩。