论文标题
事件提取的开放式唱歌式论证角色预测
Open-Vocabulary Argument Role Prediction for Event Extraction
论文作者
论文摘要
事件提取中的参数角色是指事件与参与其中的参数之间的关系。尽管事件提取取得了长足的进步，但现有的研究仍然取决于领域专家预先定义的角色。这些研究扩展到新兴事件类型或新领域时，暴露出明显的弱点。因此，需要更多的关注和努力来自动自定义参数角色。在本文中，我们定义了这一必不可少但不足的任务：开放式录像带论点角色预测。该任务的目的是推断给定事件类型的一组参数角色。我们为这项任务提出了一个小说的无监督框架。具体而言，我们将角色预测问题作为填写任务提出，并提示预先训练的语言模型生成候选角色。通过提取和分析候选论点，进一步合并并选择了特定事件的角色。为了标准化此任务的研究，我们从Wikippedia收集了一个新的事件提取数据集，其中包括142个具有丰富语义的自定义参数角色。在此数据集上，Rolepred的表现要大量优于现有方法。源代码和数据集可在我们的GitHub存储库中找到：https：//github.com/yzjiao/rolepred
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies still depend on roles pre-defined by domain experts. These studies expose obvious weakness when extending to emerging event types or new domains without available roles. Therefore, more attention and effort needs to be devoted to automatically customizing argument roles. In this paper, we define this essential but under-explored task: open-vocabulary argument role prediction. The goal of this task is to infer a set of argument roles for a given event type. We propose a novel unsupervised framework, RolePred for this task. Specifically, we formulate the role prediction problem as an in-filling task and construct prompts for a pre-trained language model to generate candidate roles. By extracting and analyzing the candidate arguments, the event-specific roles are further merged and selected. To standardize the research of this task, we collect a new event extraction dataset from WikiPpedia including 142 customized argument roles with rich semantics. On this dataset, RolePred outperforms the existing methods by a large margin. Source code and dataset are available on our GitHub repository: https://github.com/yzjiao/RolePred
