Counterfactual Generative Smoothing for Imbalanced Natural Language Classification

Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented (O-) artifacts from dataset imbalance, by proposing a Counterfac-tual Generative Smoothing approach on both feature-space anddata-space, namely CGSf and CGSd. Our technical contribution issmoothing majority and minority observations, by sampling a ma-jority seed and transferring to minority. Our proposed approachesnot only outperform state-of-the-arts in both synthetic and real-lifedatasets, they effectively reduce both artifact types.