  Argument labeling of explicit discourse relations is a challenging task. The
state of the art systems achieve slightly above 55% F-measure but require
hand-crafted features. In this paper, we propose a Long Short Term Memory
(LSTM) based model for argument labeling. We experimented with multiple
configurations of our model. Using the PDTB dataset, our best model achieved an
F1 measure of 23.05% without any feature engineering. This is significantly
higher than the 20.52% achieved by the state of the art RNN approach, but
significantly lower than the feature based state of the art systems. On the
other hand, because our approach learns only from the raw dataset, it is more
widely applicable to multiple textual genres and languages.
