e-CARE: a New Dataset for Exploring Explainable Causal Reasoning. (arXiv:2205.05849v1 [cs.AI])

Web: http://arxiv.org/abs/2205.05849

Understanding causality has vital importance for various Natural Language
Processing (NLP) applications. Beyond the labeled instances, conceptual
explanations of the causality can provide deep understanding of the causal
facts to facilitate the causal reasoning process. However, such explanation
information still remains absent in existing causal reasoning resources. In
this paper, we fill this gap by presenting a human-annotated explainable CAusal
REasoning dataset (e-CARE), which contains over 21K causal reasoning questions,
together with natural language formed explanations of the causal questions. …

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