KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
This addresses the data scarcity issue in event causality detection for NLP applications, offering a significant improvement over existing methods.
The paper tackles the problem of limited hand-labeled data for event causality detection by proposing KnowDis, a knowledge-enhanced data augmentation framework using distant supervision, which outperforms previous methods by a large margin on benchmark datasets like EventStoryLine and Causal-TimeBank.
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.