  Recently, commonsense reasoning in text generation has attracted much
attention. Generative commonsense reasoning is the task that requires machines,
given a group of keywords, to compose a single coherent sentence with
commonsense plausibility. While existing datasets targeting generative
commonsense reasoning focus on everyday scenarios, it is unclear how well
machines reason under specific geographical and temporal contexts. We formalize
this challenging task as SituatedGen, where machines with commonsense should
generate a pair of contrastive sentences given a group of keywords including
geographical or temporal entities. We introduce a corresponding English dataset
consisting of 8,268 contrastive sentence pairs, which are built upon several
existing commonsense reasoning benchmarks with minimal manual labor.
Experiments show that state-of-the-art generative language models struggle to
generate sentences with commonsense plausibility and still lag far behind human
performance. Our dataset is publicly available at
https://github.com/yunx-z/situated_gen.
