Argument Paraphrasing for Author Obfuscation
Conversational agents shall be able to provide realistic arguments in an opinionated discussion. The training data for learning such natural argumentation are ideally real statements provided publicly by human speakers. However, such opinionated statements often incorporate very specific stylistic elements of an individual speaker with a distinct personality. Being able to reidentify such opinion source through a conversational agent’s response is ethically controversial. Therefore, the aim of this project is to explore to which extent agents can learn to present arguments and opinions in a neutral manner that doesn’t allow to track down the original training data authors.