Uncovering Implicit Inferences for Improved Relational Argument Mining

Ameer Saadat-Yazdi, Jeff Z. Pan, Nadin Kökciyan

Abstract: Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations between arguments (such as attack, support, and neutral) is a challenging task because two arguments may be related to each other via implicit inferences. This task often requires external commonsense knowledge to discover how one argument relates to another. State-of-the-art methods, however, rely on pre-defined knowledge graphs, and thus might not cover target argument pairs well. We introduce a new generative neuro-symbolic approach to finding inference chains that connect the argument pairs by making use of the Commonsense Transformer (COMET). We evaluate our approach on three datasets for both the two-label (attack/support) and three-label (attack/support/neutral) tasks. Our approach significantly outperforms the state-of-the-art, by 2-5% in F1 score, on all three datasets.