Event extraction is typically modeled as a multi-class classification problem
where event types and argument roles are treated as atomic symbols. These
approaches are usually limited to a set of pre-defined types. We propose a
novel event extraction framework that uses event types and argument roles as
natural language queries to extract candidate triggers and arguments from the
input text. With the rich semantics in the queries, our framework benefits from
the attention mechanisms to better capture the semantic correlation between the
event types or argument roles and the input text. Furthermore, the
query-and-extract formulation allows our approach to leverage all available
event annotations from various ontologies as a unified model. Experiments on
ACE and ERE demonstrate that our approach achieves state-of-the-art performance
on each dataset and significantly outperforms existing methods on zero-shot
event extraction.

本文提出了一种新的事件提取框架，该框架使用事件类型和参数角色作为自然语言查询，从输入文本中提取候选触发器和参数。通过查询中的丰富语义，我们的框架受益于注意机制，以更好地捕捉事件类型或参数角色与输入文本之间的语义相关性。此外，查询和提取公式允许我们的方法将来自各种本体的所有可用事件注释作为统一模型来利用。在 ACE 和 ERE 上的实验表明，本方法在每个数据集上均取得了最先进的性能，并在零 - shot 事件提取上显著优于现有方法。