论文标题
基于生成模板的事件提取的动态前缀调节
Dynamic Prefix-Tuning for Generative Template-based Event Extraction
论文作者
论文摘要
我们以基于模板的有条件生成的方式考虑以生成方式提取事件。尽管将事件提取的任务作为提示的序列生成问题的趋势上升，但这些基于一代的方法面临两个重大挑战，包括使用次优提示和静态事件类型信息。在本文中，我们通过将上下文信息与特定于类型的前缀集成，以学习每个上下文的上下文特定前缀，以动态前缀（GTEE-DYNPREF）提出一种基于生成模板的事件提取方法。实验结果表明，我们的模型通过ACE 2005上的基于最新的分类模型Oneie实现了竞争成果，并在ERE上取得了最佳性能。此外，我们的模型被证明可以有效地适合新类型的事件。
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.
