  The possible consequences for the same context may vary depending on the
situation we refer to. However, current studies in natural language processing
do not focus on situated commonsense reasoning under multiple possible
scenarios. This study frames this task by asking multiple questions with the
same set of possible endings as candidate answers, given a short story text.
Our resulting dataset, Possible Stories, consists of more than 4.5K questions
over 1.3K story texts in English. We discover that even current strong
pretrained language models struggle to answer the questions consistently,
highlighting that the highest accuracy in an unsupervised setting (60.2%) is
far behind human accuracy (92.5%). Through a comparison with existing datasets,
we observe that the questions in our dataset contain minimal annotation
artifacts in the answer options. In addition, our dataset includes examples
that require counterfactual reasoning, as well as those requiring readers'
reactions and fictional information, suggesting that our dataset can serve as a
challenging testbed for future studies on situated commonsense reasoning.
