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.

本研究旨在解决自然语言处理中针对多种可能情况下的常识推理问题，通过构建Possible Stories数据集，对预训练语言模型的性能进行评估，结果表明其正确率远低于人类。该数据集包含需要反事实推理，读者反应和虚构信息的示例，可作为未来研究的测试基准。

可能的故事：在多种可能的情景下评估情境常识推理