SocialIQA: Commonsense Reasoning about Social Interactions

22 Apr 2019  ·  Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, Yejin Choi ·

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).

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Datasets


Introduced in the Paper:

SIQA

Used in the Paper:

CommonsenseQA BookCorpus WSC COPA

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering COPA BERT-large 340M Accuracy 80.8 # 36
Question Answering COPA BERT-SocialIQA 340M Accuracy 83.4 # 33
Question Answering SIQA Random chance baseline Accuracy 33.3 # 20
Question Answering SIQA BERT-base 110M (fine-tuned) Accuracy 63.1 # 10
Question Answering SIQA GPT-1 117M (fine-tuned) Accuracy 63 # 11
Question Answering SIQA BERT-large 340M (fine-tuned) Accuracy 64.5 # 9
Coreference Resolution Winograd Schema Challenge BERT-large 340M Accuracy 67 # 39
Coreference Resolution Winograd Schema Challenge BERT-SocialIQA 340M Accuracy 72.5 # 30

Methods


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