Deep learning models perform poorly on tasks that require commonsense
reasoning, which often necessitates some form of world-knowledge or reasoning
over information not immediately present in the input. We collect human
explanations for commonsense reasoning in the form of natural language
sequences and highlighted annotations in a new dataset called Common Sense
Explanations (CoS-E). We use CoS-E to train language models to automatically
generate explanations that can be used during training and inference in a novel
Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the
state-of-the-art by 10% on the challenging CommonsenseQA task. We further study
commonsense reasoning in DNNs using both human and auto-generated explanations
including transfer to out-of-domain tasks. Empirical results indicate that we
can effectively leverage language models for commonsense reasoning.

使用自然语言序列和突出的注释收集人类的常识推理解释，形成一个名为 CoS-E 的新数据集。利用 CoS-E 训练语言模型，在 Commonsense Auto-Generated Explanation (CAGE) 框架中生成解释，可用于训练和推理。实验结果表明能够有效利用语言模型进行常识推理。