  Available corpora for Argument Mining differ along several axes, and one of
the key differences is the presence (or absence) of discourse markers to signal
argumentative content. Exploring effective ways to use discourse markers has
received wide attention in various discourse parsing tasks, from which it is
well-known that discourse markers are strong indicators of discourse relations.
To improve the robustness of Argument Mining systems across different genres,
we propose to automatically augment a given text with discourse markers such
that all relations are explicitly signaled. Our analysis unveils that popular
language models taken out-of-the-box fail on this task; however, when
fine-tuned on a new heterogeneous dataset that we construct (including
synthetic and real examples), they perform considerably better. We demonstrate
the impact of our approach on an Argument Mining downstream task, evaluated on
different corpora, showing that language models can be trained to automatically
fill in discourse markers across different corpora, improving the performance
of a downstream model in some, but not all, cases. Our proposed approach can
further be employed as an assistive tool for better discourse understanding.
