English

SocialIQA: Commonsense Reasoning about Social Interactions

Computation and Language 2019-09-10 v3

Abstract

We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could hear"). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).

Cite

@article{arxiv.1904.09728,
  title  = {SocialIQA: Commonsense Reasoning about Social Interactions},
  author = {Maarten Sap and Hannah Rashkin and Derek Chen and Ronan LeBras and Yejin Choi},
  journal= {arXiv preprint arXiv:1904.09728},
  year   = {2019}
}

Comments

the first two authors contributed equally; accepted to EMNLP 2019; camera ready version

R2 v1 2026-06-23T08:45:58.857Z