  We present a neural architecture for modeling argumentative dialogue that
explicitly models the interplay between an Opinion Holder's (OH's) reasoning
and a challenger's argument, with the goal of predicting if the argument
successfully changes the OH's view. The model has two components: (1)
vulnerable region detection, an attention model that identifies parts of the
OH's reasoning that are amenable to change, and (2) interaction encoding, which
identifies the relationship between the content of the OH's reasoning and that
of the challenger's argument. Based on evaluation on discussions from the
Change My View forum on Reddit, the two components work together to predict an
OH's change in view, outperforming several baselines. A posthoc analysis
suggests that sentences picked out by the attention model are addressed more
frequently by successful arguments than by unsuccessful ones.
