The objective of original QA model minimizes the sum of the negative log probabilities: where N is the number of examples in training set, s i and e i are the start and end indices of the i-th example, respectively.
for down-weighting noise, we combine this QA loss function with weighted confidence score, based on function f (CS(p, q)): in initial steps of training, the confidence score is not reliable, and may cause unstable training.