We train ten models with different seeds for each setup (universal and three personalised for MeD and TeD), applying the same parameters for all of them: one hidden layer of 100 units, variational dropout rates for input and hidden layer of 0.25, and the nadam optimiser (Dozat, 2016).
we also investigate continuous updates of the suggestion model during the annotation process, but focus on a task in which annotators require vast training and domain expertise.