  Context is everything, even in commonsense moral reasoning. Changing contexts
can flip the moral judgment of an action; "Lying to a friend" is wrong in
general, but may be morally acceptable if it is intended to protect their life.
  We present ClarifyDelphi, an interactive system that learns to ask
clarification questions (e.g., why did you lie to your friend?) in order to
elicit additional salient contexts of a social or moral situation. We posit
that questions whose potential answers lead to diverging moral judgments are
the most informative. Thus, we propose a reinforcement learning framework with
a defeasibility reward that aims to maximize the divergence between moral
judgments of hypothetical answers to a question. Human evaluation demonstrates
that our system generates more relevant, informative and defeasible questions
compared to competitive baselines. Our work is ultimately inspired by studies
in cognitive science that have investigated the flexibility in moral cognition
(i.e., the diverse contexts in which moral rules can be bent), and we hope that
research in this direction can assist both cognitive and computational
investigations of moral judgments.
