  Deep NLP models have been shown to learn spurious correlations, leaving them
brittle to input perturbations. Recent work has shown that counterfactual or
contrastive data -- i.e. minimally perturbed inputs -- can reveal these
weaknesses, and that data augmentation using counterfactuals can help
ameliorate them. Proposed techniques for generating counterfactuals rely on
human annotations, perturbations based on simple heuristics, and meaning
representation frameworks. We focus on the task of creating counterfactuals for
question answering, which presents unique challenges related to world
knowledge, semantic diversity, and answerability. To address these challenges,
we develop a Retrieve-Generate-Filter(RGF) technique to create counterfactual
evaluation and training data with minimal human supervision. Using an
open-domain QA framework and question generation model trained on original task
data, we create counterfactuals that are fluent, semantically diverse, and
automatically labeled. Data augmentation with RGF counterfactuals improves
performance on out-of-domain and challenging evaluation sets over and above
existing methods, in both the reading comprehension and open-domain QA
settings. Moreover, we find that RGF data leads to significant improvements in
a model's robustness to local perturbations.
