(2016  It is shown that augmenting at evaluation time (aggregation by voting) result in stable improvement (around +2~3% MAP and +2~6% MRR for both scenarios that either augments the training data or not)-this shows that increasing the paraphrastic diversity of the answer candidates could potentially make the system more robust.
augmenting the training set does not yield such improvements-we speculate that this may introduce some noise to the training data.