For benchmark data, the models are trained using an early-stop strategy with maximum epoch set to 50 and patience (i.e., number of epochs with no improvement on the dev set for both SF and IC) to 6.
we proposed a series of end-to-end multi-task learning architectures, in which task, task group and task universe features are learned nonredundantly.