Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates---a competitive format where the winner is determined by audience votes---and show how the outcome of a debate depends on aspects of conversational flow. In particular, we find that winners tend to make better use of a debate's interactive component than losers, by actively pursuing their opponents' points rather than promoting their own ideas over the course of the conversation.
Existing research on argumentation strategies has largely focused on exploiting the structure of monologic arguments @cite_3 , like those of persuasive essays @cite_12 @cite_9 . In addition, has examined the effectiveness of arguments in the context of a forum where people invite others to challenge their opinions. We complement this line of work by looking at the relative persuasiveness of participants in extended conversations as they exchange arguments over multiple turns.
@cite_9 @cite_12 @cite_3
2250309026 2119707623
In this paper, we present a novel approach for identifying argumentative discourse structures in persuasive essays. The structure of argumentation consists of several components (i.e. claims and premises) that are connected with argumentative relations. We consider this task in two consecutive steps. First, we identify the components of arguments using multiclass classification. Second, we classify a pair of argument components as either support or non-support for identifying the structure of argumentative discourse. For both tasks, we evaluate several classifiers and propose novel feature sets including structural, lexical, syntactic and contextual features. In our experiments, we obtain a macro F1-score of 0.726 for identifying argument components and 0.722 for argumentative relations. Argumentation schemes are structures or templates for various kinds of arguments. Given the text of an argument with premises and conclusion identified, we classify it as an instance of one of five common schemes, using features specific to each scheme. We achieve accuracies of 63--91 in one-against-others classification and 80--94 in pairwise classification (baseline = 50 in both cases).