  Claims are the central component of an argument. Detecting claims across
different domains or data sets can often be challenging due to their varying
conceptualization. We propose to alleviate this problem by fine tuning a
language model using a Reddit corpus of 5.5 million opinionated claims. These
claims are self-labeled by their authors using the internet acronyms IMO/IMHO
(in my (humble) opinion). Empirical results show that using this approach
improves the state of art performance across four benchmark argumentation data
sets by an average of 4 absolute F1 points in claim detection. As these data
sets include diverse domains such as social media and student essays this
improvement demonstrates the robustness of fine-tuning on this novel corpus.
