提出新框架，捕捉事件中隐含和分散的语义信息。
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments
- 将事件抽取转为文本生成任务，支持复杂论元提取
- 新数据集DiscourseEE中51.2%论元为隐含，17.4%为分散
- 适合研究医疗对话、复杂事件建模的学者使用
以往研究将事件论元抽取视为跨度提取问题，假设论元为连续文本片段。本文重新审视事件抽取定义，引入两种现有框架无法建模的论元类型：隐含论元（需通过上下文推断）和分散论元（信息散布于全文）。为支持三类论元的抽取，构建新数据集DiscourseEE，包含7,464条来自在线健康讨论的论元标注。其中51.2%为隐含论元，17.4%为分散论元。同时将论元抽取建模为文本生成任务，并对主流模型进行综合评估，揭示生成式事件抽取的关键挑战。数据与代码已公开。
原文摘要 · Abstract (English)
Prior works formulate the extraction of event-specific arguments as a span extraction problem, where event arguments are explicit -- i.e. assumed to be contiguous spans of text in a document. In this study, we revisit this definition of Event Extraction (EE) by introducing two key argument types that cannot be modeled by existing EE frameworks. First, implicit arguments are event arguments which are not explicitly mentioned in the text, but can be inferred through context. Second, scattered arguments are event arguments that are composed of information scattered throughout the text. These two argument types are crucial to elicit the full breadth of information required for proper event modeling. To support the extraction of explicit, implicit, and scattered arguments, we develop a novel dataset, DiscourseEE, which includes 7,464 argument annotations from online health discourse. Notably, 51.2% of the arguments are implicit, and 17.4% are scattered, making DiscourseEE a unique corpus for complex event extraction. Additionally, we formulate argument extraction as a text generation problem to facilitate the extraction of complex argument types. We provide a comprehensive evaluation of state-of-the-art models and highlight critical open challenges in generative event extraction. Our data and codebase are available at https://omar-sharif03.github.io/DiscourseEE.
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