  While state-of-the-art NLP models have been achieving the excellent
performance of a wide range of tasks in recent years, important questions are
being raised about their robustness and their underlying sensitivity to
systematic biases that may exist in their training and test data. Such issues
come to be manifest in performance problems when faced with out-of-distribution
data in the field. One recent solution has been to use counterfactually
augmented datasets in order to reduce any reliance on spurious patterns that
may exist in the original data. Producing high-quality augmented data can be
costly and time-consuming as it usually needs to involve human feedback and
crowdsourcing efforts. In this work, we propose an alternative by describing
and evaluating an approach to automatically generating counterfactual data for
data augmentation and explanation. A comprehensive evaluation on several
different datasets and using a variety of state-of-the-art benchmarks
demonstrate how our approach can achieve significant improvements in model
performance when compared to models training on the original data and even when
compared to models trained with the benefit of human-generated augmented data.
