USING MODULAR ARCHITECTURES TO PREDICT CHANGE OF BELIEFS IN ONLINE DEBATES

Researchers studying persuasion have mostly focused on modeling arguments to understand how people’s beliefs can change. However, in order to convince an audience the speakers usually adapt their speech. This can be seen often in political campaigns when ideas are phrased - framed - in different ways according to the geo-graphical region the candidate is in. This practice suggests that, in order to change people’s beliefs, it is important to take into account their previous perspectives and topics of interest.

In this work we propose ChangeMyStance, a novel task to predict if a user would change their mind after being exposed to opposing views on a particular subject. This setting takes into account users’ beliefs before a debate, thus modeling their preconceived notions about the topic. Moreover, we explore a new approach to solve the problem, where the task is decomposed into ”simpler” problems. Breaking the main objective into several tasks allows to build expert modules that combined produce better results. This strategy significantly outperforms a BERT end-to-end model over the same inputs.