GLUCOSE: GeneraLized and COntextualized Story Explanations

By Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, David W. Buchanan, L. Berkowitz, Or Biran, Jennifer Chu-Carroll. 2020

131 citations recorded by the source corpus.

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 440K 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.

Subfields: Artificial Intelligence, Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022, background): “…seems to be crucial for LLMs (Kaplan et al., 2020; Chowd-hery et al., 2022), which poses a challenge: text datasets rich in social intelligence and interactions are not easily found naturally due to reporting biases, and they are costly to create (Rashkin et al., 2018; Mostafazadeh et al., 2020).”
Teach Me to Explain: A Review of Datasets for Explainable NLP (2021, methodology): “We do not survey datasets that only fall into this broader, unbounded class such as PIQA , ART , and GLUCOSE , leaving discussion of how best to deﬁne this class and its utility for the goals of E X NLP to future work.”
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing (2021, methodology): “While ad-hoc methods can improve quality [20, 143, 84], an effective and generalizable method is to collect annotations in two stages.”
CIDER: Commonsense Inference for Dialogue Explanation and Reasoning (2021, methodology): “In this context, it is also important to highlight the fundamental differences between our dataset and GLUCOSE (Mostafazadeh et al., 2020).”
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations (2022, background): “…2021) has explored different forms of explanations ranging from extractive ratio-nales (Zaidan et al., 2007; DeYoung et al., 2020), semi-structured, and structured explanations (Jansen et al., 2019; Mostafazadeh et al., 2020; Saha et al., 2021) to free-text explanations (Camburu et al., 2018).”
Natural Language Processing with Commonsense Knowledge: A Survey (2021, background): “The GLUCOSE [24] dataset comprises approximately 670,000 annotations that encode causal explanations grounded in narrative contexts, focusing on events, motivations, and emotional states.”
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021, background): “6), and (3) unlike semi-structured templates (Ye et al., 2020; Mostafazadeh et al., 2020) or extractive rationales (Zaidan et al., 2007; Lei et al., 2016; Yu et al., 2019; DeYoung et al., 2020), they allow for more ﬂexi-bility and expressiveness.”