Uncovering Implicit Inferences for Improved Relational Argument Mining

Argument mining seeks to extract arguments and their structure from unstructured texts. Identifying relations (such as attack, support, and neutral) between argumentative units is a challenging task because two units may be related to each other via implicit inferences. These inferences often rely on external commonsense knowledge to discover how one argumentative unit relates to another. State-of-the-art methods, however, rely on predefined knowledge graphs, and thus might not cover target pairs of argumentative units well. We introduce a new generative approach to finding inference chains that connect these 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 two out of the three datasets with minor improvements on the remaining one.