Deep Multimodal Learning for Mining and Generation of Arguments

forthcoming

Argumentation is carried out every day on multiple platforms and media, no longer exchanged only between humans, but also in human-machine dialogues. The computational analysis of argumentation is vital to ensure logical soundness and fairness in argument exchanges. The state of the art falls short of fulfilling these needs and does not offer a robust handling of incomplete and implicit arguments, and of multimodal argumentation involving verbal and nonverbal cues. My project will solve this timely scientific and societal challenge by developing computational methods for making multimodal argumentation in digitally mediated human interactions more intelligible. Our key move is to go beyond the traditional supervised argument mining approach to analyse argumentation from text. To do so, we introduce a new foundation for argument mining based on unsupervised learning to capture both the explicit and the latent features of human argumentation. A new family of methods for the analysis of multimodal argumentation, to consider both verbal (text, audio) and nonverbal (image, video, social context) features, will empower this paradigm change, breaking new ground in Artificial Intelligence (AI) and beyond. The project will further define novel generative methods that reflect the latent properties of human argumentation and generate robust arguments to be put forward in human-machine interactions. The benefits of this research are far-reaching. First, it will significantly strengthen AI-based argumentation analysis by automatically identifying fallacies and biases while improving fairness in argument generation. Second, argument-based digital mediation will enhance transparency in reaching consensus during deliberative democracy processes. By revealing underlying argumentation reasoning patterns and harnessing both verbal and nonverbal contexts, this research will revolutionize the ability to evaluate evidence and form reasoned judgments in crucial areas like politics and law