Theoretically, the discriminator should be trained using (context, true question, true answer) triples as positive instances and (context, generated question, generated answer) triples as the negative instances.
we find that training a discriminator using such positive instances makes it very strong since the generator would have to not only generate real looking questions but also generate real looking answers to fool the discriminator.