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What Makes a Good Argument? Understanding and Predicting High Quality Arguments Using NLP Methods

Machine Learning and Friends Lunch
November 9
Computer Science Building, Room 150/151

Abstract:

Debate and deliberation play essential roles in politics and civil discourse. While argument content and linguistic style both affect debate outcomes, limited work has been done on studying the interplay between the two. In the first part of this 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 audience. By experimenting with Oxford-style debates, our model predicts audience-adjudicated winners with 74% accuracy, significantly outperforming models based on 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.

 

In the second part of my talk, I will present our recent study on retrieving diverse types of supporting arguments from relevant documents for user-specified topics. We find that human writers often use different types of arguments to promote persuasiveness, which can be characterized with different linguistic features. We then show how to leverage argument type to assist the task of supporting argument detection. I will also discuss our follow-up work on automatic argument generation. 

 

Bio:

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 machine learning algorithms and statistical models for natural language processing (NLP) tasks, including abstractive text summarization, language generation, argumentation mining, information extraction, and their applications in interdisciplinary subjects (e.g., computational social science). Lu and her collaborators received an outstanding short paper award at ACL 2017 and a best paper nomination award at SIGDIAL 2012. Her group's work is funded by National Science Foundation (NSF), Intelligence Advanced Research Projects Activity (IARPA), and several industry gifts (Toutiao AI Lab, and NVIDIA GPU program). More information about her research can be found at www.ccs.neu.edu/home/luwang/.