  Selective rationalization has become a common mechanism to ensure that
predictive models reveal how they use any available features. The selection may
be soft or hard, and identifies a subset of input features relevant for
prediction. The setup can be viewed as a co-operate game between the selector
(aka rationale generator) and the predictor making use of only the selected
features. The co-operative setting may, however, be compromised for two
reasons. First, the generator typically has no direct access to the outcome it
aims to justify, resulting in poor performance. Second, there's typically no
control exerted on the information left outside the selection. We revise the
overall co-operative framework to address these challenges. We introduce an
introspective model which explicitly predicts and incorporates the outcome into
the selection process. Moreover, we explicitly control the rationale complement
via an adversary so as not to leave any useful information out of the
selection. We show that the two complementary mechanisms maintain both high
predictive accuracy and lead to comprehensive rationales.
