  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. This paper details two concrete
contributions. First, we present our platform for effectively crowdsourcing
GLUCOSE data at scale, which uses semi-structured templates to elicit causal
explanations. Using this platform, we collected a total of ~670K specific
statements and general rules that capture implicit commonsense knowledge about
everyday situations. Second, we show that existing knowledge resources and
pretrained language models do not include or readily predict GLUCOSE's rich
inferential content. However, when state-of-the-art neural models are trained
on this knowledge, they can start to make commonsense inferences on unseen
stories that match humans' mental models.
