Retrieval-guided Counterfactual Generation for QA

Understand

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.