  Modelling persuasion strategies as predictors of task outcome has several
real-world applications and has received considerable attention from the
computational linguistics community. However, previous research has failed to
account for the resisting strategies employed by an individual to foil such
persuasion attempts. Grounded in prior literature in cognitive and social
psychology, we propose a generalised framework for identifying resisting
strategies in persuasive conversations. We instantiate our framework on two
distinct datasets comprising persuasion and negotiation conversations. We also
leverage a hierarchical sequence-labelling neural architecture to infer the
aforementioned resisting strategies automatically. Our experiments reveal the
asymmetry of power roles in non-collaborative goal-directed conversations and
the benefits accrued from incorporating resisting strategies on the final
conversation outcome. We also investigate the role of different resisting
strategies on the conversation outcome and glean insights that corroborate with
past findings. We also make the code and the dataset of this work publicly
available at https://github.com/americast/resper.
