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Available corpora for Argument Mining differ along several axes, and one ofthe key differences is the presence (or absence) of discourse markers to signalargumentative content. Exploring effective ways to use discourse markers hasreceived wide attention in various discourse parsing tasks, from which it iswell-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 suchthat all relations are explicitly signaled. Our analysis unveils that popularlanguage models taken out-of-the-box fail on this task; however, whenfine-tuned on a new heterogeneous dataset that we construct (includingsynthetic and real examples), they perform considerably better. We demonstratethe impact of our approach on an Argument Mining downstream task, evaluated ondifferent corpora, showing that language models can be trained to automaticallyfill in discourse markers across different corpora, improving the performanceof a downstream model in some, but not all, cases. Our proposed approach canfurther be employed as an assistive tool for better discourse understanding.

Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?