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Deep Multimodal Learning for Mining and Generation of Arguments

Project description

A multimodal paradigm for argument mining

Argumentation occurs daily across numerous media platforms, extending beyond human-to-human interaction to include human–machine dialogues. While automated argumentation analysis ensures logical validity and fairness in debates, current systems struggle to handle implicit or incomplete arguments. Additionally, these systems have difficulty processing complex, multimodal interactions that combine both verbal and non-verbal cues. To address this, the ERC-funded PANDORA project aims to make multimodal argumentation in digital environments more understandable by introducing new computational techniques. To advance argument mining, it will leverage an unsupervised approach that models both explicit and latent aspects of human reasoning. By bridging verbal and non-verbal (visual, social) elements, this framework offers a new multimodal paradigm that breaks new ground in AI research.

Objective

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

Fields of science (EuroSciVoc)

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Keywords

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Topic(s)

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2025-COG

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Host institution

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 999 651,00
Address
RUE MICHEL ANGE 3
75794 PARIS
France

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Region
Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 999 651,00

Beneficiaries (1)

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