The main challenge in cross-domain persuasion detection lies in the vast differences in vocabulary observed across different outlets and contexts. Superficially, an argument made on social media will look nothing like an opinion presented in the Supreme Court, but the latent factors that make an argument persuasive are common across all settings. Regardless of domain, persuasive arguments tend to use sound reasoning and present solid evidence, build on the credibility and authority of the source, or appeal to the emotions and beliefs of the audience. In this paper, we show that simply encoding the different argumentative components and their semantic types can significantly improve a language model’s ability to detect persuasion across vastly different domains.