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Modern models of event causality detection (ECD) are mainly based onsupervised learning from small hand-labeled corpora. However, hand-labeledtraining data is expensive to produce, low coverage of causal expressions andlimited in size, which makes supervised methods hard to detect causal relationsbetween events. To solve this data lacking problem, we investigate a dataaugmentation framework for ECD, dubbed as Knowledge Enhanced Distant DataAugmentation (KnowDis). Experimental results on two benchmark datasetsEventStoryLine corpus and Causal-TimeBank show that 1) KnowDis can augmentavailable training data assisted with the lexical and causal commonsenseknowledge for ECD via distant supervision, and 2) our method outperformsprevious methods by a large margin assisted with automatically labeled trainingdata.

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision