M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and Transcripts
Argumentation mining aims at extracting, analysing and modelling people’s arguments, but large, high-quality annotated datasets are limited, and no multimodal datasets exist for this task. In this paper, we present M-Arg, a multimodal argument mining dataset with a corpus of US 2020 presidential debates, annotated through crowd-sourced annotations. This dataset allows models to be trained to extract arguments from natural dialogue such as debates using information like the intonation and rhythm of the speaker. Our dataset contains 7 hours of annotated US presidential debates, 6527 utterances and 4104 relation labels, and we report results from different baseline models, namely a text-only model, an audio-only model and multimodal models that extract features from both text and audio. With accuracy reaching 0.86 in multimodal models, we find that audio features provide added value with respect to text-only models.
Code (1)
Tasks
Argument MiningRhythmSimilar Papers 제목 키워드 기반
Multimodal Argument Mining: A Case Study in Political Debates
We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on i…
Argument MiningYes, 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 …
Argument MiningLeveraging Context for Multimodal Fallacy Classification in Political Debates
In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates. Our approach uses pretraine…
Argument MiningLogical FallaciesWho Argues What? Joint Argument-Entity Detection and Classification in Political Debates
Political debates are often analyzed through Argument Mining (AM) to investigate the key arguments that drive them. However, political arguments are rarely interpretable from argumentative spans alone, as claims and prem…
Argument MiningDEBISS: a Corpus of Individual, Semi-structured and Spoken Debates
The process of debating is essential in our daily lives, whether in studying, work activities, simple everyday discussions, political debates on TV, or online discussions on social networks. The range of uses for debates…
Speaker DiarizationArgument Mining