Emotion Recognition Based Preference Modelling in Argumentative Dialogue Systems

Abstract

Within this work, we present an approach to model the opinion of a human towards a specific topic in a finegrained way by using weighted bipolar argumentation graphs. In addition, we discuss how the therefore required rating of related aspects can be collected by means of emotion recognition techniques and discuss an application scenario based on the state-of-the-art Argumentative Dialogue System EVA in which the proposed techniques can be applied.

How to Win Arguments

“…Our results showed the predictive power of the proposed prediction model and that the trained logical policy performs comparably well as the optimal policy based on probabilistic rules. In our future work, we will explore different techniques to implicitly estimate the persuasive effect of the presented arguments on the user without explicit feedback based on the approaches discussed in [19] and the prediction model presented in [28]. Since argumentation as a whole is highly subjective [10], we aim to extend the system to allow the agents to adapt both investigated aspects (how and what) to the user simultaneously in order to determine the most effective combination of the different aspects.…”

Paving the way towards counterfactual generation in argumentative conversational agents

“…Further-more, such argumentation-based agents are considered to push the boundaries of the present-day conversational agents towards more human-like interaction (Dignum and Bex, 2018). In combination with recent advances in deep learning and reinforcement learning, the use of argumentation as a theoretical basis for conversational agents opens prospects for a new era of generative conversational agents (Rosenfeld and Kraus, 2016;Rach et al, 2019).…”