  One of the major goals in automated argumentation mining is to uncover the
argument structure present in argumentative text. In order to determine this
structure, one must understand how different individual components of the
overall argument are linked. General consensus in this field dictates that the
argument components form a hierarchy of persuasion, which manifests itself in a
tree structure. This work provides the first neural network-based approach to
argumentation mining, focusing on the two tasks of extracting links between
argument components, and classifying types of argument components. In order to
solve this problem, we propose to use a joint model that is based on a Pointer
Network architecture. A Pointer Network is appealing for this task for the
following reasons: 1) It takes into account the sequential nature of argument
components; 2) By construction, it enforces certain properties of the tree
structure present in argument relations; 3) The hidden representations can be
applied to auxiliary tasks. In order to extend the contribution of the original
Pointer Network model, we construct a joint model that simultaneously attempts
to learn the type of argument component, as well as continuing to predict links
between argument components. The proposed joint model achieves state-of-the-art
results on two separate evaluation corpora, achieving far superior performance
than a regular Pointer Network model. Our results show that optimizing for both
tasks, and adding a fully-connected layer prior to recurrent neural network
input, is crucial for high performance.
