Generative Data Augmentation for Commonsense Reasoning

Sentence: Natalie went to see a patient in the hospital but not Betty because _ was in town.

Do you think this question was made by a human or by G-DAUGc?


Generative Data AUGmentation for Commonsense Reasoning (G-DAUGc) is a method for generating additional training data for commonsense models, improving accuracy without requiring additional annotations. Learn more in our paper.

Here, you can view some training examples G-DAUGc produced for a commonsense task called the Winograd Schema Challenge, where the goal is to choose the word that best fits in the blank. We trained the model on a large dataset of Winograd-style questions called Winogrande, and we've mixed in some of those original examples here for comparison. Help us improve the model by guessing which is which!

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The Winogrande dataset is the property of its creators and is used here under the CC BY 2.0 license.