  As NLP models become increasingly integral to decision-making processes, the
need for explainability and interpretability has become paramount. In this
work, we propose a framework that achieves the aforementioned by generating
semantically edited inputs, known as counterfactual interventions, which change
the model prediction, thus providing a form of counterfactual explanations for
the model. We test our framework on two NLP tasks - binary sentiment
classification and topic classification - and show that the generated edits are
contrastive, fluent and minimal, while the whole process remains significantly
faster that other state-of-the-art counterfactual editors.
