  We explore the use of residual networks and neural attention for argument
mining and in particular link prediction. The method we propose makes no
assumptions on document or argument structure. We propose a residual
architecture that exploits attention, multi-task learning, and makes use of
ensemble. We evaluate it on a challenging data set consisting of user-generated
comments, as well as on two other datasets consisting of scientific
publications. On the user-generated content dataset, our model outperforms
state-of-the-art methods that rely on domain knowledge. On the scientific
literature datasets it achieves results comparable to those yielded by
BERT-based approaches but with a much smaller model size.
