论文标题
ea $^2 $ e：提高与文档级别参数提取的事件意识的一致性
EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
论文作者
论文摘要
事件与文档相关。在每次言语理论的动机上，我们假设参与者倾向于在同一文档中的多个事件中扮演一致的角色。然而，关于文档级事件参数提取的最新工作是孤立的每个事件模型，因此在事件中提取的论点之间会导致不一致，这将进一步导致下游应用程序的差异，例如事件知识基础人群，问题答案和假设产生。在这项工作中，我们将事件参数一致性作为文档级设置下事件事件关系的约束。为了提高一致性，我们介绍了事件感知参数提取（EA $^2 $ e）模型，并具有增强的培训和推理上下文。与基线方法相比，Wikivents和ACE2005数据集的实验结果证明了EA $^2 $ E的有效性。
Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore causes inconsistency among extracted arguments across events, which will further cause discrepancy for downstream applications such as event knowledge base population, question answering, and hypothesis generation. In this work, we formulate event argument consistency as the constraints from event-event relations under the document-level setting. To improve consistency we introduce the Event-Aware Argument Extraction (EA$^2$E) model with augmented context for training and inference. Experiment results on WIKIEVENTS and ACE2005 datasets demonstrate the effectiveness of EA$^2$E compared to baseline methods.
