Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classification subtasks - (1) Subtask-A: Argument Stance Classification; (2) Subtask-B: Image Persuasiveness Classification. The former determines the stance of a tweet containing an image and a piece of text toward a controversial topic (e.g., gun control and abortion). The latter determines whether the image makes the tweet text more persuasive. The shared task received 31 submissions for Subtask-A and 21 submissions for Subtask-B from 9 different teams across 6 countries. The top submission in Subtask-A achieved an F1-score of 0.8647 while the best submission in Subtask-B achieved an F1-score of 0.5561.Comment: In The 10th Argument Mining Workshop, held in conjunction with The Conference on Empirical Methods in Natural Language Processing (EMNLP), December 202

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

International audienceIn this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics

DISPUTool -- A tool for the Argumentative Analysis of Political Debates

International audiencePolitical debates are the means used by political candidates to put forward and justify their positions in front of the electors with respect to the issues at stake. Argument mining is a novel research area in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the argumentative content of political speeches. More precisely, DISPUTool allows to explore and automatically identify argumentative components over the 39 political debates from the last 50 years of US presidential campaigns (1960-2016)

Ethos and Pragmatics

Ethos, the speaker’s image in speech is one of the three means of persuasion e stablished by Aristotle’s Rhetoric and is often studied in a loose way. Many scholars develop lists of self-images (ethos of a leader, modesty ethos, etc.), but few explain how one arrives at these types of ethos. This is precisely what the inferential approach described here intends to do. Considering, like many discourse analysts, that ethos is consubstantial with speech, this paper provides an overview of various types and subtypes of ethos and highlights how these can be inferred from the discourse. Mainly, we would like to point out that what the speaker says about him or herself is only a part of what has been called “said ethos”: inferential processes triggered by what the speaker says about collectivities, opponents, or the audience also help construct an ethos. This tool will be applied to analyze a corpus of Donald Trump’s tweets of 6 January 2021, the day of the assault on the Capitol. As the notion of inference is essential in creating ethos, the paper pleads for the integration of the study of this rhetorical notion in the field of pragmatics

The use of Basque language in debates in the Basque Parliament (2012-2020)

[EU] Legebiltzarretan, deliberazio demokrazien testuinguruan egindako hitzaldien azterketa garrantzitsua da, hitzaldi horiek hertsiki lotuta baitaude ekintza politikoarekin eta legegintza ekimenak garatzeko arrazoien azalpenarekin. Bestalde, azken urteotan, azterketa automatizatuei esker gehitu egin dira diskurtsoa aztertzeko aukerak, eta informazio bolumen handiak prozesatu daitezke. Testuinguru horretan, euskarak Eusko Legebiltzarrean duen erabilera aztertzen da artikulu honetan, bi legegintzaldiko (hots, 2012-2016 eta 2016-2020 legegintzaldietako) bilkuren akten azterketa automatizatuan oinarrituta. Emaitzek adierazten dutenez, euskaraz egindako agerraldien guztizko bolumena eguneroko erabilerari dagokiona baino handixeagoa da, baina adierazi beharra dago euskarari agerikotasuna emateko helburua duten protokolo erabilerak eta erabilera sinbolikoak ere identifikatu direla.[EN] The analysis of interventions in parliament, in the context of deliberative democracies, is relevant because they are closely linked to political action and to the exposition of the reasons why legislative initiatives are developed. For its part, in recent years, automated content analysis has made it possible to process large volumes of information. In this context, this article studies the use of Basque in the Basque Parliament based on the automated analysis of the minutes of the sessions during two legislatures (2012-2016 and 2016-2020). The results indicate that, in the total volume of interventions, those carried out in Basque are slightly above their daily use, although tendencies towards their formal and symbolic use that seek to make their presence visible are also identified

Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates

Political debates offer a rare opportunity for citizens to compare the candidates’ positions on the most controversial topics of the campaign. Thus they represent a natural application scenario for Argument Mining. As existing research lacks solid empirical investigation of the typology of argument components in political debates, we fill this gap by proposing an Argument Mining approach to political debates. We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims. We then propose two tasks: (1) identifying the argumentative components in such debates, and (2) classifying them as premises and claims. We show that feature-rich SVM learners and Neural Network architectures outperform standard baselines in Argument Mining over such complex data. We release the new corpus USElecDeb60To16 and the accompanying software under free licenses to the research community