  We explore the use of residual networks and neural attention for multiple
argument mining tasks. We propose a residual architecture that exploits
attention, multi-task learning, and makes use of ensemble, without any
assumption on document or argument structure. We present an extensive
experimental evaluation on five different corpora of user-generated comments,
scientific publications, and persuasive essays. Our results show that our
approach is a strong competitor against state-of-the-art architectures with a
higher computational footprint or corpus-specific design, representing an
interesting compromise between generality, performance accuracy and reduced
model size.
