GLUCOSE: GeneraLized and COntextualized Story Explanations

TL;DR

GLUCOSE introduces a large dataset of implicit commonsense causal knowledge grounded in narratives, enabling AI to better understand and infer human-like mental models of everyday situations.

Contribution

The paper presents a scalable crowdsourcing platform for collecting causal explanations and demonstrates that training neural models on this data improves their ability to make commonsense inferences.

Findings

Collected ~670K causal statements and rules.

Existing resources lack GLUCOSE's rich inferential content.

Neural models trained on GLUCOSE can infer unseen story details.

Abstract

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.