iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability

Understand

Causality knowledge is vital to building robust AI systems.

• Deep learning models often perform poorly on tasks that require causal reasoning, which is often derived using some form of commonsense knowledge not immediately available in the input but implicitly inferred by humans.
• Prior work has unraveled spurious observational biases that models fall prey to in the absence of causality.
• While language representation models preserve contextual knowledge within learned embeddings, they do not factor in causal relationships during training.