Using Asymmetric Associations for Commonsense Causality Detection

Abstract

Human actions in this world are based on exploiting knowledge of causality. Humans find it easy to connect a cause to the subsequent effect but formal reasoning about causality has proved to be a difficult task in automated NLP applications because it requires rich knowledge of all the relevant events and circumstances. Automated approaches to detecting causal connections attempt to partially capture this knowledge using commonsense reasoning based on lexical and semantics constraints. However, their performance is limited by the lack of sufficient breadth of commonsense knowledge to draw causal inferences. This paper presents a commonsense causality detection system using a new semantic measure based on asymmetric associations on the Choice Of Plausible Alternatives (COPA) task. When evaluated on three COPA benchmark datasets, the causality detection system using asymmetric association based measures demonstrates a superior performance to other symmetric measures.