  Arguing without committing a fallacy is one of the main requirements of an
ideal debate. But even when debating rules are strictly enforced and fallacious
arguments punished, arguers often lapse into attacking the opponent by an ad
hominem argument. As existing research lacks solid empirical investigation of
the typology of ad hominem arguments as well as their potential causes, this
paper fills this gap by (1) performing several large-scale annotation studies,
(2) experimenting with various neural architectures and validating our working
hypotheses, such as controversy or reasonableness, and (3) providing linguistic
insights into triggers of ad hominem using explainable neural network
architectures.
