How to Win Arguments - Empowering Virtual Agents to Improve their Persuasiveness (EVA)

Whether speakers are perceived as credible depends not only on the contents of their speech, but also to a large extent on how a message is conveyed. Human communication is not only based on speech, but also comprises other channels, such as gestures, postures, facial expressions and gaze, all of which influence the perception of the audience. In the EVA project, we investigate typical conversational patterns in public debates including the content and structure of arguments, but also how they are communicated to an audience. Moreover, we include tactical dialogue acts (so called dodge moves), such as a change of topics or subterfuges, that are often used in real debates. To this end, we simulate argumentation dialogues between humans through embodied conversational agents. As an application domain, we focus on political discourse. Since political debates have an enormous influence on shaping people's mind and attitudes, it is of particular importance to provide people with tools that help them explore the argumentation space by presenting arguments in different forms and thus support their decision-making processes. The use of embodied conversational agents is an audiovisual form of presentation that has been less explored in the area of argumentation mining. It allows us to present arguments in a way that is intuitive and reveals the effect of rational and non-rational elements in a debate. The verbal and nonverbal behaviors of the agents is determined using a combination of a rule-based approach that is informed by theories on argumentation and a data-driven approach that is informed by corpora of multimodal debates between humans. The arguments for the virtual agents are automatically extracted from an ontology that is created using argument mining techniques. We rely on Reinforcement Learning (RL) to optimize the agents' argumentation strategies in an interaction with a simulated opponent. The EVA proposal has been prepared within the Priority Program Robust Argumentation Machines (RATIO). It addresses the following core question of RATIO: How to present arguments intuitively to users in order to support decision-making processes? The EVA project has an interdisciplinary character. It combines research on multimodal behavior synthesis and analysis, argumentation mining and dialogue management.