Generative Data Augmentation for Commonsense Reasoning
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!
The Winogrande dataset is the property of its creators and is used here under the CC BY 2.0 license.