Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes

Abstract

Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.

Asking too much? The rhetorical role of questions in political discourse

“…Inevitably, drawing causal lessons from observational data is difficult. Moving forward, experimental tests of insights gathered through such explorations would enable us to establish causal effects of question-asking rhetoric, perhaps offering prescriptive insights into questioning strategies for objectives such as information-seeking (Dillman, 1978), request-making (Althoff et al, 2014;Mitra and Gilbert, 2014) and persuasion (Tan et al, 2016;Zhang et al, 2016;Wang et al, 2017). Tables 2 and 3 provide further examples of representative questios and motifs from each of the eight question types we induce on our dataset of parliamentary question periods.…”

What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

“…As a fast growing sub-field of computational argumentation mining [35,41], previous work in this area mostly work on the identification of convincing arguments [13,44] and viewpoints [14,19] from varying argumentation genres, such as social media discussions [37], political debates [4], and student essays [6]. In this line, many existing studies focus on crafting hand-made features [37,44], such as wordings and topic strengths [43,53], echoed words [2], semantic and syntactic rules [15,30], participants' personality [42], argument interactions and structure [29], and so forth. These methods, however, require labor-intensive feature engineering process, and hence have limited generalization abilities to new domains.…”

“…Similar trends are observed on both datasets and we only discuss the results on CMV dataset due to the space limitation. To investigate topic effects, we follow Wang et al [43] to identify strong argument topics when the topic likelihood is larger than a pre-defined threshold (set to 0.2 here). 8 Then in Figure 7(a), we show how the number of strong argument topics distribute over winning arguments compared with the losing ones.…”

Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning

“…Argument quality evaluation is a task closely related to AES, which involves evaluation of argumentative texts with various grains (argumentlevel, post-level, etc.). Tan et al (2016); Wei et al (2016a); Wang et al (2017) make use of linguistic features to evaluate the persuasiveness of ar-guments in online forums. Wei et al (2016b);Ji et al (2018) consider features from the perspectives of argumentation interaction between participants.…”