AMPERSAND: Argument Mining for PERSuAsive oNline Discussions


Argumentation is a type of discourse where speakers try to persuade their audience about the reasonableness of a claim by presenting supportive arguments. Most work in argument mining has focused on modeling arguments in monologues. We propose a computational model for argument mining in online persuasive discussion forums that brings together the micro-level (argument as product) and macro-level (argument as process) models of argumentation. Fundamentally, this approach relies on identifying relations between components of arguments in a discussion thread. Our approach for relation prediction uses contextual information in terms of fine-tuning a pre-trained language model and leveraging discourse relations based on Rhetorical Structure Theory. We additionally propose a candidate selection method to automatically predict what parts of one’s argument will be targeted by other participants in the discussion. Our models obtain significant improvements compared to recent state-of-the-art approaches using pointer networks and a pre-trained language model.

In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing
Alyssa Hwang
Alyssa Hwang
PhD Student

I am a first-year PhD student in the Department of Computer and Information Science at the University of Pennsylvania. I am particularly interested in the intersections of Natural Language Processing, Linguistics, and Psychology, especially expanding NLU resources for nonstandard English. I am supported by the NSF Graduate Research Fellowship Program. I earned my BS in Computer Science at Columbia University, where I conducted research and wrote an undergraduate thesis with Prof. Kathleen McKeown.