In other words, the training set will have a sample selection bias that can impair prediction performance.
to mitigate this, we propose to sample questions from a biased sample distribution D. to achieve D, we introduce the weighted loss q,a) which represents how likely it is to observe (q, a) under D compared to D. In this setting, we can show that the generalization error under D is the same as that under D: given appropriate weights w(q, a), we modify our loss-function in order to compute an unbiased estimator of the generalization error.