  A class of explainable NLP models for reasoning tasks support their decisions
by generating free-form or structured explanations, but what happens when these
supporting structures contain errors? Our goal is to allow users to
interactively correct explanation structures through natural language feedback.
We introduce MERCURIE - an interactive system that refines its explanations for
a given reasoning task by getting human feedback in natural language. Our
approach generates graphs that have 40% fewer inconsistencies as compared with
the off-the-shelf system. Further, simply appending the corrected explanation
structures to the output leads to a gain of 1.2 points on accuracy on
defeasible reasoning across all three domains. We release a dataset of over
450k graphs for defeasible reasoning generated by our system at
https://tinyurl.com/mercurie .
