  Improving model generalization on held-out data is one of the core objectives
in commonsense reasoning. Recent work has shown that models trained on the
dataset with superficial cues tend to perform well on the easy test set with
superficial cues but perform poorly on the hard test set without superficial
cues. Previous approaches have resorted to manual methods of encouraging models
not to overfit to superficial cues. While some of the methods have improved
performance on hard instances, they also lead to degraded performance on easy
instances. Here, we propose to explicitly learn a model that does well on both
the easy test set with superficial cues and hard test set without superficial
cues. Using a meta-learning objective, we learn such a model that improves
performance on both the easy test set and the hard test set. By evaluating our
models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation,
we show that our proposed method leads to improved performance on both the easy
test set and the hard test set upon which we observe up to 16.5 percentage
points improvement over the baseline.
