Different Flavors of Attention Networks for Argument Mining

Argument mining is a rising area of Natural Language Pro- cessing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be ex- ploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the man- ual effort involved in these tasks, taking into account hetero- geneous types of texts and topics. In this work, we evaluate different attention mechanisms applied over a state-of-the-art architecture for sequence labeling. We assess the impact of different flavors of attention in the task of argument compo- nent detection over two datasets: essays and legal domain. We show that attention not models the problem better but also supports interpretability.