Contextualized Soft Prompts for Extraction of Event Arguments
Contextualized Soft Prompts for Extraction of Event Arguments
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DOI：
10.18653/v1/2023.findings-acl.266
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Chien Van Nguyen;Hieu Man;Thien Huu Nguyen
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文献类型：
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作者：
Chien Van Nguyen;Hieu Man;Thien Huu Nguyen
事件论元抽取（EAE）是事件抽取的一个子任务，其目标是识别文本中事件的实体提及的角色。目前针对这一问题的最新方法探索了基于语义的方法来提示预先训练的语言模型关于输入上下文的参数。然而，现有的基于提示的方法主要依赖于离散的和手动设计的提示，其不能针对每个示例利用特定的上下文来改进定制以获得最佳性能。此外，当前提示的离散性质阻止了来自多个外部文档的相关上下文的合并以丰富EAE的提示。为此，我们提出了一种新的基于EAE的方法，引入软提示，以方便编码的个别例子上下文和多个相关的文件，以提高EAE。我们广泛评估了EAE基准数据集上的方法，以证明其具有最先进性能的优点。
Event argument extraction (EAE) is a sub-task of event extraction where the goal is to identify roles of entity mentions for events in text. The current state-of-the-art approaches for this problem explore prompt-based meth-ods to prompt pre-trained language models for arguments over input context. However, existing prompt-based methods mainly rely on discrete and manually-designed prompts that cannot exploit specific context for each example to improve customization for optimal performance. In addition, the discrete nature of current prompts prevents the incorporation of relevant context from multiple external documents to enrich prompts for EAE. To this end, we propose a novel prompt-based method for EAE that introduces soft prompts to facilitate the encoding of individual example context and multiple relevant documents to boost EAE. We extensively evaluate the proposed method on benchmark datasets for EAE to demonstrate its benefits with state-of-the-art performance.