  Humans give contrastive explanations that explain why an observed event
happened rather than some other counterfactual event (the contrast case).
Despite the important role that contrastivity plays in how people generate and
evaluate explanations, this property is largely missing from current methods
for explaining NLP models. We present Minimal Contrastive Editing (MiCE), a
method for generating contrastive explanations of model predictions in the form
of edits to inputs that change model outputs to the contrast case. Our
experiments across three tasks -- binary sentiment classification, topic
classification, and multiple-choice question answering -- show that MiCE is
able to produce edits that are not only contrastive, but also minimal and
fluent, consistent with human contrastive edits. We demonstrate how MiCE edits
can be used for two use cases in NLP system development -- uncovering dataset
artifacts and debugging incorrect model predictions -- and thereby illustrate
that generating contrastive explanations is a promising research direction for
model interpretability.
