Argument Relation Classification through Discourse Markers and Adversarial Training (2024)

Abstract: Argument relation classification (ARC) identifies supportive, contrasting and neutral relations between argumentative units. The current approaches rely on transformer architectures which have proven to be more effective than traditional methods based on hand-crafted linguistic features. In this paper, we introduce DISARM, which advances the state of the art with a training procedure combining multi-task and adversarial learning strategies. By jointly solving the ARC and discourse marker detection tasks and aligning their embedding spaces into a unified latent space, DISARM outperforms the accuracy of existing approaches.

## introduction argument relation classification (arc) is a crucial task in argument mining and aims to automatically identify relations between argumentative units to understand whether they support each other, are in opposition, or have no dependency @xcite @xcite . it can be applied in various domains, such as political debates, legal and juridical cases, business negotiations. in these scenarios, arc facilitates the understanding and evaluation of complex discussions by identifying logical connections and assessing their argumentative coherence and effectiveness. in literature, arc is typically conceived as a classification problem where pairs of argument units are categorized into predefined relation classes. for example, an arc model is asked to recognize that the sentences in the first row of traditional arc approaches rely on the extraction of hand-crafted linguistic features, which derive from the identification of specific discourse ## conclusion we presented disarm, an argument relation classifier that injects knowledge of discourse markers into a pre-trained roberta model via multi-task and adversarial learning. the experimental evaluation shows that this model outperforms previous state-of-the-art methods and learns discriminative sentence embeddings supporting the task.