  This work examines the use of contradiction in natural language inference
(NLI) for question answering (QA). Typically, NLI systems help answer questions
by determining if a potential answer is \emph{entailed} (supported) by some
background context. But is it useful to also determine if an answer contradicts
the context? We test this in two settings, multiple choice and extractive QA,
and find that systems that incorporate contradiction can do slightly better
than entailment-only systems on certain datasets. However, the best
performances come from using contradiction, entailment, and QA model confidence
scores together. This has implications for the deployment of QA systems in
domains such as medicine and science where safety is an issue.
