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Deep learning models perform poorly on tasks that require commonsensereasoning, which often necessitates some form of world-knowledge or reasoningover information not immediately present in the input. We collect humanexplanations for commonsense reasoning in the form of natural languagesequences and highlighted annotations in a new dataset called Common SenseExplanations (CoS-E). We use CoS-E to train language models to automaticallygenerate explanations that can be used during training and inference in a novelCommonsense Auto-Generated Explanation (CAGE) framework. CAGE improves thestate-of-the-art by 10% on the challenging CommonsenseQA task. We further studycommonsense reasoning in DNNs using both human and auto-generated explanationsincluding transfer to out-of-domain tasks. Empirical results indicate that wecan effectively leverage language models for commonsense reasoning.

Explain Yourself! Leveraging Language Models for Commonsense Reasoning