Relying on the feature vector o g , it attempts to accurately predict the probability of the token x triggering an event for all event classes, i.e.,ŷ = D(o g , θ d ), and assigns x to the most probable class c (iffŷ c > ∀ŷc,c = c).
g and D cooperate with each other during training, developing the parameters θ g and θ d with the same goal -to minimize the performance loss L(ŷ, y) in the detection task: where, y denotes the ground-truth probability distribution over event classes, and L indicates the deviation of the prediction from the ground truth.