Explain Yourself! Leveraging Language Models for Commonsense Reasoning
This addresses the challenge of commonsense reasoning in AI, which is crucial for applications requiring world-knowledge, though it is incremental as it builds on existing language models and datasets.
The paper tackles the problem of deep learning models performing poorly on commonsense reasoning tasks by introducing a framework that uses language models to generate explanations, resulting in a 10% improvement on the CommonsenseQA benchmark.
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.