  Contrastive explanations for understanding the behavior of black box models
has gained a lot of attention recently as they provide potential for recourse.
In this paper, we propose a method Contrastive Attributed explanations for Text
(CAT) which provides contrastive explanations for natural language text data
with a novel twist as we build and exploit attribute classifiers leading to
more semantically meaningful explanations. To ensure that our contrastive
generated text has the fewest possible edits with respect to the original text,
while also being fluent and close to a human generated contrastive, we resort
to a minimal perturbation approach regularized using a BERT language model and
attribute classifiers trained on available attributes. We show through
qualitative examples and a user study that our method not only conveys more
insight because of these attributes, but also leads to better quality
(contrastive) text. Quantitatively, we show that our method outperforms other
state-of-the-art methods across four data sets on four benchmark metrics.
