State-of-the-art systems for argumentation mining are supervised, thus relying on training data containing manually annotated argument components and the relationships between them. To eliminate the reliance on annotated data, we present a novel approach to unsupervised argument mining. The key idea is to bootstrap from a small set of argument components automatically identified using simple heuristics in combination with reliable contextual cues. Results on a Stab and Gurevych’s corpus of 402 essays show that our unsupervised approach rivals two supervised baselines in performance and achieves 73.5-83.7% of the performance of a state-of-the-art neural approach.

We propose the first lightly-supervised approach to scoring an argument’s persuasiveness. Key to our approach is the novel hypothesis that lightly-supervised persuasiveness scoring is possible by explicitly modeling the major errors that negatively impact persuasiveness. In an evaluation on a new annotated corpus of online debate arguments, our approach rivals its fully-supervised counterparts in performance by four scoring metrics when using only 10% of the available training instances.