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.

中文翻译：

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葡萄糖：一般化和语境化的故事解释

当人类阅读或聆听时，他们会做出隐含的常识性推论，从而构建他们对发生的事情和原因的理解。作为迈向可以构建类似心智模型的 AI 系统的一步，我们引入了 GLUCOSE，这是一个包含隐性常识因果知识的大规模数据集，编码为关于世界的因果迷你理论，每个理论都以叙事语境为基础。为了构建 GLUCOSE，我们利用认知心理学来确定因果解释的十个维度，重点关注事件、状态、动机和情绪。每个 GLUCOSE 条目都包含一个特定于故事的因果陈述，并与从该陈述概括的推理规则配对。本文详细介绍了两个具体的贡献。首先，我们展示了我们的平台，用于有效地大规模众包 GLUCOSE 数据，该平台使用半结构化模板来引出因果解释。使用这个平台，我们收集了总共约 670K 的特定陈述和一般规则，这些陈述和一般规则捕获了有关日常情况的隐含常识。其次，我们表明现有的知识资源和预训练的语言模型不包括或容易预测 GLUCOSE 丰富的推理内容。然而，当最先进的神经模型接受了这些知识的训练后，它们就可以开始对与人类心智模型相匹配的未见故事进行常识性推断。