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Detecting arguments in online interactions is useful to understand howconflicts arise and get resolved. Users often use figurative language, such assarcasm, either as persuasive devices or to attack the opponent by an adhominem argument. To further our understanding of the role of sarcasm inshaping the disagreement space, we present a thorough experimental setup usinga corpus annotated with both argumentative moves (agree/disagree) and sarcasm.We exploit joint modeling in terms of (a) applying discrete features that areuseful in detecting sarcasm to the task of argumentative relationclassification (agree/disagree/none), and (b) multitask learning forargumentative relation classification and sarcasm detection using deep learningarchitectures (e.g., dual Long Short-Term Memory (LSTM) with hierarchicalattention and Transformer-based architectures). We demonstrate that modelingsarcasm improves the argumentative relation classification task(agree/disagree/none) in all setups.

"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space