  Argument mining automatically identifies and extracts the structure of
inference and reasoning conveyed in natural language arguments. To the best of
our knowledge, most of the state-of-the-art works in this field have focused on
using tree-like structures and linguistic modeling. But, these approaches are
not able to model more complex structures which are often found in online
forums and real world argumentation structures. In this paper, a novel
methodology for argument mining is proposed which employs attention-based
embeddings for link prediction to model the causational hierarchies in typical
argument structures prevalent in online discourse.
