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

TL;DR

KnowDis is a data augmentation framework that leverages lexical and causal commonsense knowledge via distant supervision to improve event causality detection, outperforming previous methods on benchmark datasets.

Contribution

This paper introduces KnowDis, a novel knowledge-enhanced data augmentation method for event causality detection using distant supervision, addressing data scarcity issues.

Findings

KnowDis effectively augments training data with knowledge-based labels.

The method significantly outperforms previous approaches on benchmark datasets.

Knowledge integration improves causal relation detection accuracy.

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.