Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

This lecture explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, focusing on natural language processing applications. It delves into the challenges of providing machines with large-scale commonsense knowledge and the importance of connecting world knowledge components to understand complex situations. The instructor presents a framework for training knowledge models from language models trained on raw text, emphasizing the need for neural architectures that simulate the state of the world. The lecture also discusses the dynamic construction of reasoning graphs and the application of commonsense reasoning in story generation.