  Argument Mining is the research area which aims at extracting argument
components and predicting argumentative relations (i.e.,support and attack)
from text. In particular, numerous approaches have been proposed in the
literature to predict the relations holding between the arguments, and
application-specific annotated resources were built for this purpose. Despite
the fact that these resources have been created to experiment on the same task,
the definition of a single relation prediction method to be successfully
applied to a significant portion of these datasets is an open research problem
in Argument Mining. This means that none of the methods proposed in the
literature can be easily ported from one resource to another. In this paper, we
address this problem by proposing a set of dataset independent strong neural
baselines which obtain homogeneous results on all the datasets proposed in the
literature for the argumentative relation prediction task. Thus, our baselines
can be employed by the Argument Mining community to compare more effectively
how well a method performs on the argumentative relation prediction task.
