Published 2019 | Version v2

Social Interaction QA

  • 1. Paul G. Allen School of Computer Science & Engineering, Seattle, WA, USA

Description

Description

Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people's actions and their social implications. For example, given an action like "Jesse saw a concert" and a question like "Why did Jesse do this?", humans can easily infer that Jesse wanted "to see their favorite performer" or "to enjoy the music", and not "to see what's happening inside" or "to see if it works". The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models' abilities to reason about the social implications of everyday events and situations.

Files

Files (8.6 MB)

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md5:affc925326f542c589a03275cffb4bc8
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Variables

Name Description
context A context describing a sitation
question A question probing commonsense reasoning about motivation, emotional reactions etc.
answerA The first answer choice
answerB The second answer choice
answerC The third answer choice

Details

Resource type Open dataset
Title Social Interaction QA
Alternative title Social IQA
Creators
  • Sap, Maarten1
  • Rashkin, Hannah1
  • Chen, Derek1
  • Le Bras, Ronan1
  • Choi, Yejin1
  • Publisher Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing
    Year of publication 2019
    Research fields Business Administration Economics Psychology Sociology Political Science Economic & Social History Communication Sciences Educational Research Other
    Size 0.01 GB
    Formats JSON format (.json)
    External resource https://leaderboard.allenai.org/socialiqa/submissions/get-started

    Additional details

    Related works

    Is cited by
    Journal article: 10.48550/arXiv.2210.13312 (DOI)