GLUCOSE: GeneraLized and COntextualized Story Explanations

Understand

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why.

• As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context.
• To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions.
• Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement.