Modeling Inter Round Attack of Online Debaters for Winner Prediction

In a debate, two debaters with opposite stances put forward arguments to fight for their viewpoints. Debaters organize their arguments to support their proposition and attack opponents' points. The common purpose of debating is to persuade the opponents and the audiences to agree with the mentioned propositions. Previous works have investigated the issue of identifying which debater is more persuasive. However, modeling the interaction of arguments between rounds is rarely discussed. In this paper, we focus on assessing the overall performance of debaters in a multi-round debate on online forums. To predict the winner in a multi-round debate, we propose a novel neural model that is aimed at capturing the interaction of arguments by exploiting raw text, structure information, argumentative discourse units (ADUs), and the relations among ADUs. Experimental results show that our model achieves competitive performance compared with the existing models, and is capable of extracting essential argument relations during a multi-round debate by leveraging argumentative structure and attention mechanism.