Deborah Dore

The increasing amount of political debates and politics-related discussions calls for the definition of novel computational methods to automatically analyse such content with the final goal of lightening up political deliberation to citizens. However, the specificity of the political language and the argumentative form of these debates (employing hidden communication strategies and leveraging implicit arguments) make this task very challenging, even for current general-purpose pre-trained Language Models (LMs). To address this, we introduce a novel pre-trained LM for political discourse language called RooseBERT. Pre-training a LM on a specialised domain presents different technical and linguistic challenges, requiring extensive computational resources and large-scale data. RooseBERT has been trained on large political debate and speech corpora (11GB) in English. To evaluate its performances, we fine-tuned it on multiple downstream tasks related to political debate analysis, i.e., stance detection, sentiment analysis, argument component detection and classification, argument relation prediction and classification, policy classification, named entity recognition (NER). Our results show significant improvements over general-purpose LMs on the majority of these tasks, highlighting how domain-specific pre-training enhances performance in political debate analysis.

Multimodal Argument Mining aims at combining different modalities (i.e., text, image, video, audio) to achieve a better understanding of verbal and non-verbal argumentation. Despite significant progress in extracting and analysing argumentation structures from text, few approaches have addressed the challenge of combining different modalities in this analysis. This remains a highly unexplored but urgent topic, given the increasing impact of multimedia data in society. In this paper, we tackle this question by proposing a multimodal approach to the stance detection task. Starting with the ImageArg-2023 shared task dataset, which comprises tweets paired with images on the controversial topics of abortion and gun control, we propose the following contributions: \textiti) we present an updated version of the ImageArg dataset where we fixed some issues ensuring a high Inter-Annotator Agreement, and \textitii) we propose a modular architecture for multimodal stance detection which outperforms SoA approaches on this task. In addition, we quantify and discuss how each modality contributes to the classification task. This allows us to gain a better understanding of the role that each modality plays in multimodal AM.

Argument Mining (AM) aims at detecting argumentation structures (i.e., premises and claims linked by attack and support relations) in text. A natural application domain is political debates, where uncovering the hidden dynamics of a politician’s argumentation strategies can help the public to identify fallacious and propagandist arguments. Despite the few approaches proposed in the literature to apply AM to political debates, this application scenario is still challenging, and, more precisely, concerning the task of predicting the relation holding between two argument components. Most of AM relation prediction approaches only consider the textual content of the argument component to identify and classify the argumentative relation holding among them (i.e., support, attack), and they mostly ignore the structural knowledge that arises from the overall argumentation graph. In this paper, we propose to address the relation prediction task in AM by combining the structural knowledge provided by a Knowledge Graph Embedding Model with the contextual knowledge provided by a fine-tuned Large Language Model. Our experimental setting is grounded on a standard AM benchmark of televised political debates of the US presidential campaigns from 1960 to 2020. Our extensive experimental setting demonstrates that integrating these two distinct forms of knowledge (i.e., the textual content of the argument component and the structural knowledge of the argumentation graph) leads to novel pathways that outperform existing approaches in the literature on this benchmark and enhance the accuracy of the predictions.

This paper introduces and evaluates a novel web-based application designed to identify and repair fallacious arguments in political debates. DISPUTool 3.0 offers a comprehensive tool for argumentation analysis of political debate, in- tegrating state-of-the-art natural language pro- cessing techniques to mine and classify argu- ment components and relations. DISPUTool 3.0 builds on the ElecDeb60to20 dataset, cover- ing US presidential debates from 1960 to 2020. In this paper, we introduce a novel task which is integrated as a new module in DISPUTool, i.e., the automatic detection and classification of fal- lacious arguments, and the automatic repairing of such misleading arguments. The goal is to show to the user a tool which not only identifies fallacies in political debates, but it also shows how the argument looks like once the veil of fallacy falls down. An extensive evaluation of the module is addressed employing both auto- mated metrics and human assessments. With the inclusion of this module, DISPUTool 3.0 ad- vances even more user critical thinking in front of the augmenting spread of such nefarious kind of content in political debates and beyond. The tool is publicly available here: https: //3ia-demos.inria.fr/disputool/