Belief-based Generation of Argumentative Claims

TL;DR

This paper introduces a method for generating argumentative claims tailored to specific beliefs, advancing computational argumentation by incorporating audience beliefs into claim generation.

Contribution

It proposes a novel approach to belief-based claim generation by modeling beliefs through stance data and extending text generation models accordingly.

Findings

Models can adapt claims to given beliefs.

Generated claims are evaluated for informativeness.

Limitations exist in modeling beliefs solely from stances.

Abstract

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.