We thus design to minimize the following loss function: where 0<α≤1 and β>0 are two weighting parameters, y i is the relation assigned by DS for the i-th instance, N the total number of training instances, o iy i is the probability that the observed relation for the i-th instance is y i , and p iy i is the probability to predict relation y i for the i-th instance.
initially, we set α=1, and train our model completely by minimizing the loss from the prediction distribution p. we do not expect to model the noise, but focus on the prediction branch at this time.