  Disagreements are frequently studied from the perspective of either detecting
toxicity or analysing argument structure. We propose a framework of dispute
tactics that unifies these two perspectives, as well as other dialogue acts
which play a role in resolving disputes, such as asking questions and providing
clarification. This framework includes a preferential ordering among
rebuttal-type tactics, ranging from ad hominem attacks to refuting the central
argument. Using this framework, we annotate 213 disagreements (3,865
utterances) from Wikipedia Talk pages. This allows us to investigate research
questions around the tactics used in disagreements; for instance, we provide
empirical validation of the approach to disagreement recommended by Wikipedia.
We develop models for multilabel prediction of dispute tactics in an utterance,
achieving the best performance with a transformer-based label powerset model.
Adding an auxiliary task to incorporate the ordering of rebuttal tactics
further yields a statistically significant increase. Finally, we show that
these annotations can be used to provide useful additional signals to improve
performance on the task of predicting escalation.

Computation and Language;Artificial Intelligence;Computers and Society