e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
This dataset addresses the problem of missing explanation information in causal reasoning for NLP applications, but it is incremental as it builds on existing resources by adding explanations.
The authors introduced e-CARE, a dataset with over 21K causal reasoning questions and natural language explanations, to address the lack of explanation information in existing resources. Experimental results showed that generating valid explanations is challenging for state-of-the-art models, and such explanations can improve the accuracy and stability of causal reasoning models.
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. Experimental results show that generating valid explanations for causal facts still remains especially challenging for the state-of-the-art models, and the explanation information can be helpful for promoting the accuracy and stability of causal reasoning models.