  Defeasible reasoning is the mode of reasoning where conclusions can be
overturned by taking into account new evidence. A commonly used method in
cognitive science and logic literature is to handcraft argumentation supporting
inference graphs. While humans find inference graphs very useful for reasoning,
constructing them at scale is difficult. In this paper, we automatically
generate such inference graphs through transfer learning from another NLP task
that shares the kind of reasoning that inference graphs support. Through
automated metrics and human evaluation, we find that our method generates
meaningful graphs for the defeasible inference task. Human accuracy on this
task improves by 20% by consulting the generated graphs. Our findings open up
exciting new research avenues for cases where machine reasoning can help human
reasoning. (A dataset of 230,000 influence graphs for each defeasible query is
located at: https://tinyurl.com/defeasiblegraphs.)
