AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
This work addresses the problem of analyzing complex argument structures in online forums for researchers and practitioners in computational linguistics, though it is incremental in building on existing methods.
The paper tackles argument mining in online persuasive discussions by modeling both micro-level and macro-level argumentation, achieving significant improvements over recent state-of-the-art approaches.
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.