Data

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SWAG, or Situations with Adversarial Generations, is a dataset which contains over one hundred and thirteen thousand multiple choice questions about what comes next in certain situations. SWAG (Situations With Adversarial Generations) is a large-scale dataset for this task of grounded commonsense inference, unifying natural language inference and physically grounded reasoning. The dataset consists of 113k multiple choice questions about grounded situations. Each question is a video caption from LSMDC or ActivityNet Captions, with four answer choices about what might happen next in the scene. The correct answer is the (real) video caption for the next event in the video; the three incorrect answers are adversarially generated and human verified, so as to fool machines but not humans. We aim for SWAG to be a benchmark for evaluating grounded commonsense NLI and for learning representations.