论文标题
电子护理：用于探索可解释的因果推理的新数据集
e-CARE: a New Dataset for Exploring Explainable Causal Reasoning
论文作者
论文摘要
了解因果关系对于各种自然语言处理（NLP）应用至关重要。除了标记的实例之外，对因果关系的概念解释还可以提供对因果事实的深刻理解，以促进因果推理过程。但是，在现有的因果推理资源中，此类解释信息仍然不存在。在本文中，我们通过提出一个可解释的因果推理数据集（电子护理）来填补这一空白，该数据集包含超过21k的因果推理问题，以及对因果问题的自然语言的解释。实验结果表明，生成因果事实的有效解释对于最先进的模型仍然特别具有挑战性，并且解释信息可能有助于促进因果推理模型的准确性和稳定性。
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.
