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Recently, commonsense reasoning in text generation has attracted muchattention. Generative commonsense reasoning is the task that requires machines,given a group of keywords, to compose a single coherent sentence withcommonsense plausibility. While existing datasets targeting generativecommonsense reasoning focus on everyday scenarios, it is unclear how wellmachines reason under specific geographical and temporal contexts. We formalizethis challenging task as SituatedGen, where machines with commonsense shouldgenerate a pair of contrastive sentences given a group of keywords includinggeographical or temporal entities. We introduce a corresponding English datasetconsisting of 8,268 contrastive sentence pairs, which are built upon severalexisting commonsense reasoning benchmarks with minimal manual labor.Experiments show that state-of-the-art generative language models struggle togenerate sentences with commonsense plausibility and still lag far behind humanperformance. Our dataset is publicly available athttps://github.com/yunx-z/situated_gen.

SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning