We consider event extraction in a generative manner with template-based
conditional generation. Although there is a rising trend of casting the task of
event extraction as a sequence generation problem with prompts, these
generation-based methods have two significant challenges, including using
suboptimal prompts and static event type information. In this paper, we propose
a generative template-based event extraction method with dynamic prefix
(GTEE-DynPref) by integrating context information with type-specific prefixes
to learn a context-specific prefix for each context. Experimental results show
that our model achieves competitive results with the state-of-the-art
classification-based model OneIE on ACE 2005 and achieves the best performances
on ERE. Additionally, our model is proven to be portable to new types of events
effectively.

本文提出了一种生成式基于模板的事件抽取方法，通过将上下文信息与类型特定前缀相结合，学习每个上下文的上下文特定前缀。实验结果表明，该模型在 ACE 2005 上达到了与最先进的基于分类的 OneIE 相媲美的结果，在 ERE 上表现最好，并且该模型被证明对新类型的事件具有良好的可移植性。