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

Computation and Language