Existing works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from inter-dependencies between tasks. This paper presents a novel deep learning model to simultaneously solve the four tasks of IE in a single model (called FourIE). Compared to few prior work on jointly performing four IE tasks, FourIE features two novel contributions to capture inter-dependencies between tasks. First, at the representation level, we introduce an interaction graph between instances of the four tasks that is used to enrich the prediction representation for one instance with those from related instances of other tasks. Second, at the label level, we propose a dependency graph for the information types in the four IE tasks that captures the connections between the types expressed in an input sentence. A new regularization mechanism is introduced to enforce the consistency between the golden and predicted type dependency graphs to improve representation learning. We show that the proposed model achieves the state-of-the-art performance for joint IE on both monolingual and multilingual learning settings with three different languages.
翻译:关于信息提取的现有工作(IE)主要分别解决了四项主要任务(实体提及承认、关系提取、事件触发检测和引证提取),因此未能从任务之间的相互依存关系中受益。本文件展示了一个新的深层次学习模式,以同时用单一模式(称为 " FourIE " )解决IE的四项任务。与以前关于联合执行四项IE任务的少数工作相比,FourIE提供了两项新的贡献,以捕捉任务之间的相互依存关系。首先,在代表性层面,我们采用了一个互动图,在四项任务中,我们用四种任务来丰富预测的表示方式与其他任务的相关情况相比。第二,在标签层面,我们为四种IE任务的信息类型提出了一个依赖性图表,其中反映了投入句中表达的类别之间的联系。引入了新的规范机制,以强化黄金和预测型依赖性图表之间的一致性,从而改进代表性学习。我们显示,拟议的模型在单一语言和多语种学习环境中的联合IE取得了最先进的业绩。