  Labeling explicit discourse relations is one of the most challenging
sub-tasks of the shallow discourse parsing where the goal is to identify the
discourse connectives and the boundaries of their arguments. The
state-of-the-art models achieve slightly above 45% of F-score by using
hand-crafted features. The current paper investigates the efficacy of the
pre-trained language models in this task. We find that the pre-trained language
models, when finetuned, are powerful enough to replace the linguistic features.
We evaluate our model on PDTB 2.0 and report the state-of-the-art results in
the extraction of the full relation. This is the first time when a model
outperforms the knowledge intensive models without employing any linguistic
features.
