GLUCOSE: GeneraLized and COntextualized Story Explanations
This addresses the challenge of building AI with human-like commonsense reasoning, though it is incremental as it focuses on dataset creation and model training rather than a new paradigm.
The authors tackled the problem of AI systems lacking implicit commonsense causal knowledge by introducing GLUCOSE, a large-scale dataset of ~670K causal statements and rules grounded in narrative contexts, and showed that training state-of-the-art neural models on this data enables them to make commonsense inferences matching human mental models on unseen stories.
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.