For negative examples, we generate synthetic noises, similarly to translation/generation errors, by adopting a naive but effective method to change words or sentences in a positive example (Levy et al., 2017;Zhang et al., 2019;Yang et al., 2019).
we modify pseudo-positive labels to generate synthetic negative examples of unanswerable or wrong answer.