Aspect-Controlled Neural Argument Generation

Understand

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.