  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.
