SOURCE-LINKED INTELLIGENCE

Deep Multimodal Learning for Mining and Generation of Arguments

CORDIS · observation · Publication date unknown

ds 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 Argument Mining, Argumentation, Multimodal Argument Mining, Unsupervised Le

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recordType

award

status

SIGNED

region

EU

value

1999651

unit

EUR