Explain Yourself! Leveraging Language Models For Commonsense Reasoning

Nazneen Fatema Rajani, Bryan Mccann, Caiming Xiong, Richard Socher. In Proceedings of the Association for Computational Linguistics (ACL) 2019. Florence Italy 2019 – 71 citations

[Paper]    
Training Techniques RAG Tools Interpretability and Explainability Reinforcement Learning

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.

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