Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios

Mana Ashida, Saku Sugawara


Abstract
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.
Anthology ID:
2022.coling-1.319
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
3606–3630
Language:
URL:
https://aclanthology.org/2022.coling-1.319
DOI:
Bibkey:
Cite (ACL):
Mana Ashida and Saku Sugawara. 2022. Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3606–3630, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios (Ashida & Sugawara, COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.319.pdf
Code
 nii-cl/possible-stories
Data
CosmosQARACEROCStories