论文标题
诺迪斯：知识增强了通过远处监督的事件因果检测的增强数据增强
KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
论文作者
论文摘要
事件因果检测（ECD）的现代模型主要基于从小型手工标记的Corpora中进行的监督学习。但是，手工标记的培训数据的生产昂贵，因果表达的低覆盖范围和大小有限，这使得受监督的方法难以检测事件之间的因果关系。为了解决缺乏问题的数据，我们研究了ECD的数据增强框架，称为知识增强的遥远数据增强（KNOWDIS）。两个基准数据集的实验结果EventStoryline语料库和因果时间银行表明，1）KNOWDIS可以通过遥远的监督来增强可用的培训数据，从而有助于ECD的词汇和因果分辨率知识，而2）我们的方法优于以前的方法，可以通过自动标记的培训数据来实现先前的方法。
Modern models of event causality detection (ECD) are mainly based on supervised learning from small hand-labeled corpora. However, hand-labeled training data is expensive to produce, low coverage of causal expressions and limited in size, which makes supervised methods hard to detect causal relations between events. To solve this data lacking problem, we investigate a data augmentation framework for ECD, dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis). Experimental results on two benchmark datasets EventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision, and 2) our method outperforms previous methods by a large margin assisted with automatically labeled training data.
