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.
@article{arxiv.1906.02361,
title = {Explain Yourself! Leveraging Language Models for Commonsense Reasoning},
author = {Nazneen Fatema Rajani and Bryan McCann and Caiming Xiong and Richard Socher},
journal= {arXiv preprint arXiv:1906.02361},
year = {2019}
}