We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model