Abstract

Debate and deliberation play essential roles in politics and government. While argument content and linguistic style both affect debate outcomes, limited work has been done on the interplay between the two. In the first part of the talk, I will present a joint model that estimates the inherent persuasive strengths of different topics, the effects of numerous linguistic features, and the interactions between the two as they affect debate audiences. By experimenting with Oxford-style debates, our model predicts audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone. We also find that winning sides employ more strong arguments (as corroborated by human judgment) and debaters all tend to shift topics to stronger ground. The model further allows us to identify the linguistic features associated with strong or weak arguments.

Biography

Lu Wang is an Assistant Professor in College of Computer and Information Science at Northeastern University since 2015. She received her Ph.D. in Computer Science from Cornell University and her bachelor degrees in Intelligence Science and Technology and Economics from Peking University. Her research mainly focuses on designing computational algorithms and statistical models for natural language processing (NLP) tasks, including text summarization, language generation, argumentation mining, and their applications in interdisciplinary subjects. Lu received an NSF CRII award in 2016 and a best paper nomination award at SIGDIAL 2012. More information about Lu‘s research can be found at www.ccs.neu.edu/home/luwang/.