  Selection of input features such as relevant pieces of text has become a
common technique of highlighting how complex neural predictors operate. The
selection can be optimized post-hoc for trained models or incorporated directly
into the method itself (self-explaining). However, an overall selection does
not properly capture the multi-faceted nature of useful rationales such as pros
and cons for decisions. To this end, we propose a new game theoretic approach
to class-dependent rationalization, where the method is specifically trained to
highlight evidence supporting alternative conclusions. Each class involves
three players set up competitively to find evidence for factual and
counterfactual scenarios. We show theoretically in a simplified scenario how
the game drives the solution towards meaningful class-dependent rationales. We
evaluate the method in single- and multi-aspect sentiment classification tasks
and demonstrate that the proposed method is able to identify both factual
(justifying the ground truth label) and counterfactual (countering the ground
truth label) rationales consistent with human rationalization. The code for our
method is publicly available.
