Specifically, given the current model of neural networks parameterized by θ, let w β (θ) denote the relative importance of negative instances to positive instances for F β -measure.
then w β (θ) can be computed as the ratio of marginal negative utility M U F β (t N (θ)) to the marginal positive utility M U F β (t P (θ)), where t P (θ) and t N (θ) are t P and t N on training data with respect to θ-parameterized model: then at each iteration of the model optimization (i.e., each step of gradient descending), we want the model to take next update step proportional to the gradient of the w β -scaled cross-entropy loss function L AS (θ) at the current point: based on the contributions that correctly predicting one more instances of each class bringing to F-measure, the training procedure dynamically adjusts its attention between positive and negative instances.