  In many domains of argumentation, people's arguments are driven by so-called
attitude roots, i.e., underlying beliefs and world views, and their
corresponding attitude themes. Given the strength of these latent drivers of
arguments, recent work in psychology suggests that instead of directly
countering surface-level reasoning (e.g., falsifying given premises), one
should follow an argumentation style inspired by the Jiu-Jitsu 'soft' combat
system (Hornsey and Fielding, 2017): first, identify an arguer's attitude roots
and themes, and then choose a prototypical rebuttal that is aligned with those
drivers instead of invalidating those. In this work, we are the first to
explore Jiu-Jitsu argumentation for peer review by proposing the novel task of
attitude and theme-guided rebuttal generation. To this end, we enrich an
existing dataset for discourse structure in peer reviews with attitude roots,
attitude themes, and canonical rebuttals. To facilitate this process, we recast
established annotation concepts from the domain of peer reviews (e.g., aspects
a review sentence is relating to) and train domain-specific models. We then
propose strong rebuttal generation strategies, which we benchmark on our novel
dataset for the task of end-to-end attitude and theme-guided rebuttal
generation and two subtasks.
