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

• URL: http://arxiv.org/abs/2010.10833v1
• Date: Wed, 21 Oct 2020 08:44:54 GMT
• Title: KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision
• Authors: Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao
• Abstract summary: We investigate a data augmentation framework for event causality detection (ECD) dubbed as Knowledge Enhanced Distant Data Augmentation (KnowDis) KnowDis can augment available training data assisted with the lexical and causal commonsense knowledge for ECD via distant supervision. Our method outperforms previous methods by a large margin assisted with automatically labeled training data.
• Score: 23.533310981207446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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