  When engaging in argumentative discourse, skilled human debaters tailor
claims to the beliefs of the audience, to construct effective arguments.
Recently, the field of computational argumentation witnessed extensive effort
to address the automatic generation of arguments. However, existing approaches
do not perform any audience-specific adaptation. In this work, we aim to bridge
this gap by studying the task of belief-based claim generation: Given a
controversial topic and a set of beliefs, generate an argumentative claim
tailored to the beliefs. To tackle this task, we model the people's prior
beliefs through their stances on controversial topics and extend
state-of-the-art text generation models to generate claims conditioned on the
beliefs. Our automatic evaluation confirms the ability of our approach to adapt
claims to a set of given beliefs. In a manual study, we additionally evaluate
the generated claims in terms of informativeness and their likelihood to be
uttered by someone with a respective belief. Our results reveal the limitations
of modeling users' beliefs based on their stances, but demonstrate the
potential of encoding beliefs into argumentative texts, laying the ground for
future exploration of audience reach.
