We obtain the unnormalized score as well as the probability distribution of the start position as: where w s 2 R h is a trainable weight vector.
similarly, we can get the probability of the end position along with its confidence score by: During training, since each sentence may contain multiple targets, we label the span boundaries for all target entities in the list T. we can obtain a vector y s 2 R (n+2) , where each element y s i indicates whether the i-th token starts a target, and also get another vector y e 2 R (n+2) for labeling the end positions.