论文标题
事件参数提取的资源增强神经模型
Resource-Enhanced Neural Model for Event Argument Extraction
论文作者
论文摘要
事件参数提取（EAE）旨在识别事件的参数，并对这些参数扮演的角色进行分类。尽管在先前的工作中做出了巨大的努力，但仍有许多挑战：（1）数据稀缺。 （2）捕获远程依赖关系，具体来说，事件触发器与遥远的事件参数之间的连接。 （3）将事件触发信息集成到候选参数表示中。对于（1），我们以不同方式使用未标记的数据进行探索。对于（2），我们建议使用可以利用依赖性解析的语法变压器来指导注意机制。对于（3），我们提出了一个具有几种类型的触发依赖性序列表示的触发感知序列编码器。我们还支持从带有黄金实体注释的文本或纯文本中提取的参数提取。英语ACE2005基准的实验表明，我们的方法实现了新的最新技术。
Event argument extraction (EAE) aims to identify the arguments of an event and classify the roles that those arguments play. Despite great efforts made in prior work, there remain many challenges: (1) Data scarcity. (2) Capturing the long-range dependency, specifically, the connection between an event trigger and a distant event argument. (3) Integrating event trigger information into candidate argument representation. For (1), we explore using unlabeled data in different ways. For (2), we propose to use a syntax-attending Transformer that can utilize dependency parses to guide the attention mechanism. For (3), we propose a trigger-aware sequence encoder with several types of trigger-dependent sequence representations. We also support argument extraction either from text annotated with gold entities or from plain text. Experiments on the English ACE2005 benchmark show that our approach achieves a new state-of-the-art.
