Uncovering Implicit Inferences for Improved Relational Argument Mining
Ameer Saadat-Yazdi, Jeff Z. Pan, Nadin Kökciyan · 2023
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.