In particular, we will use a hard-sharing architecture: the sentence is first processed by stacked BILSTMs shared across all tasks, with a task-dependent feed-forward network on the top of it, to compute each task's outputs.
to benefit from a specific parsing abstraction we will be using the concept of auxiliary tasks (Plank et al., 2016;Bingel and Søgaard, 2017;Coavoux and Crabbé, 2017), where tasks are learned together with the main task in the MTL setup even if they are not of actual interest by themselves, as they might help to find out hidden patterns in the data and lead to better generalization of the model.